Default assistant fallback in multi-assistant devices

A speech-processing system may provide access to one or more virtual assistants via an audio-controlled device. A virtual assistant may be invoked by speaking a wakeword. In some cases, a default virtual assistant may be invoked when an utterance is spoken without a preceding wakeword. Such a multi-assistant speech-processing system may make an early determination that a received utterance does not include a wakeword, and begin processing the utterance prior to completion of the utterance, thereby reducing user perceived latency. For example, the system may start a timer when a gesture and/or voice activity is detected. If no wakeword is detected after a time corresponding to speaking durations of known wakewords, the system may determine that the utterance is to be processed according to the default virtual assistant.

BACKGROUND

Speech recognition systems have progressed to the point where humans can interact with computing devices using their voices. Such systems employ techniques to identify the words spoken by a human user based on the various qualities of a received audio input. Speech recognition combined with natural language understanding processing techniques enable speech-based user control of a computing device to perform tasks based on the user's spoken commands. Speech recognition and natural language understanding processing techniques may be referred to collectively or separately herein as speech processing. Speech processing may also involve converting a user's speech into text data which may then be provided to various text-based software applications.

Speech processing may be used by computers, hand-held devices, telephone computer systems, kiosks, and a wide variety of other devices to improve human-computer interactions.

DETAILED DESCRIPTION

Speech processing systems and speech generation systems have been combined with other services to create virtual “assistants” that a user can interact with using natural language inputs such as speech, text inputs, or the like. The assistant can leverage different computerized voice-enabled technologies. Automatic speech recognition (ASR) is a field of computer science, artificial intelligence, and linguistics concerned with transforming audio data associated with speech into text representative of that speech. Similarly, natural language understanding (NLU) is a field of computer science, artificial intelligence, and linguistics concerned with enabling computers to derive meaning from text input containing natural language. ASR and NLU are often used together as part of a speech processing system, sometimes referred to as a spoken language understanding (SLU) system. Natural language generation (NLG) includes enabling computers to generate output text or other data in words a human can understand, such as sentences or phrases. Text-to-speech (TTS) is a field of computer science concerning transforming textual and/or other data into audio data that is synthesized to resemble human speech. ASR, NLU, NLG, and TTS may be used together as part of a speech-processing system. The virtual assistant can leverage the speech-processing system as well as additional applications and/or skills to perform tasks for and/or on behalf of the user.

Some voice-controlled devices can provide access to more than one speech-processing system, where each speech-processing system may provide services associated with a different virtual assistant. In such multi-assistant systems, a speech-processing system may be associated with its own wakeword. Upon detecting a representation of a wakeword in an utterance, the device may send audio data representing the utterance to the corresponding speech-processing system.

On some devices, a user may activate a microphone by pressing a button; for example, on a remote control of a television or a steering wheel of a vehicle. Upon activation of the microphone, the device may begin processing audio to identify a representation of a wakeword. If the device detects a wakeword, it may send the audio data to the corresponding speech-processing system for processing. In some cases, the device may have a default assistant that a user may invoke without a wakeword; for example, by simply speaking an utterance after activating the microphone. If the device has multiple assistants enabled, however, the device may need to process the utterance to determine that no wakeword was detected before sending the audio data to the default speech-processing system. Waiting until the end of the utterance to select a speech-processing system for processing may introduce a delay that may increase latency perceived by the user.

The systems and method described herein can reduce the user-perceived latency using one or more mechanisms to determine that an utterance includes no wakeword. For example, upon receiving an indication to begin receiving audio data (e.g., a button press or other gesture) the device may start a timer. The device may determine a speaking duration of one or more wakewords recognized by the device (e.g., an amount of time it may take to utter the wakeword in 95% of cases). The device may use the timer to determine that a duration of time corresponding to a longest speaking duration of the wakewords (and in some cases including an additional margin duration of time) has elapsed; and, if no wakeword has been detected during that time, the device may determine that the utterance is intended for the default assistant, and release the audio data for processing by the corresponding speech-processing system (e.g., without waiting for an end of speech). Thus, the default speech-processing system may begin processing the utterance within, for example, a second or two of the beginning of speech rather than after completion of the utterance.

In some implementations, a device may include a voice activity detector (VAD). A VAD may be a hardware and/or software component that can receive audio data and indicate whether the audio data includes features consistent with a human voice. The device may start the timer upon the VAD indicating the presence of human speech in audio data. In devices where the user presses a button to activate the microphone before speaking, incorporating a VAD may improve accuracy by disregarding silence after a button press and only starting the timer once voice activity is detected. This may prevent the device from timing out while a user is speaking a wakeword and/or wakeword-less speech input, but before the wakeword detector has indicated a wakeword detection.

In some implementations, the device may process the detection signal(s) from the VAD to make an early determination that the utterance does not include a wakeword. For example, the VAD may indicate a brief segment of speech, followed by a gap, and then further speech. The device may have timing information for one or more wakewords and determine that none of the wakewords include such a gap. Thus, the device may determine absence of a wakeword even before the timer has elapsed and release audio data for processing by the default speech-processing system, thereby reducing user-perceived latency even further.

These and other features of the disclosure are provided as examples, and maybe used in combination with each other and/or with additional features described herein.

FIG.1Ais a conceptual diagram illustrating components of a multi-assistant system100with default assistant fallback, according to embodiments of the present disclosure. The system100may include a device110, such as the smart TV110gpictured, in communication with one or more remote systems120via one or more computer networks199.FIG.13illustrates other example devices110that may be part of a multi-assistant system100such as a vehicle110e, a smart watch110c, a speech-detection device with display110f, etc. The device110may include various components for input and output such as one or more displays116, one or more speakers112, etc. The device110may be operated in part using a remote control105. In some implementations, the remote control105may be separate from the device110(e.g., such as a remote control for the smart TV110g), may be a separate device110(e.g., such as a smart phone110brunning a remote control app), and/or may be attached to and/or integrated within the device110(e.g., such as steering wheel-mounted controls of the vehicle110e). The remote control105may include one or more microphones114and/or buttons113. In some implementations, the button113may be touch sensitive (e.g., non-mechanical), a softkey (e.g., programmable to have different functions or trigger different operations), and/or be presented via a touchscreen, etc. At least one of the buttons113may activate the microphone114and allow the system100to receive verbal commands and requests from a user. In some implementations, the system100may detect various gestures (e.g., non-physical movements) that indicate the system100is to receive an input such as audio11. The system100may respond to the user by various means including synthesized speech (e.g., emitted by the speaker112) conveying a natural language message. Various components of the system100as described with reference toFIG.1A(as well as with reference toFIGS.5and6) may reside in the remote control105, the device110, and/or the system120. In some implementations, various components of the system100may be shared, duplicated, and/or divided between the remote control105, the device110, and/or the system120.

The device110may provide the user with access to one or more virtual assistants. A virtual assistant may be configured with certain functionalities that it can perform for and/or on behalf of the user. A virtual assistant may further be configured with certain identifying characteristics that may be output to the user to indicate which virtual assistant is receiving input (e.g., listening via the microphone114), processing (e.g., performing ASR, NLU, and/or executing actions), and/or providing output (e.g., speaking via TTS). A user may invoke a particular virtual assistant by, for example, speaking a wakeword associated with that virtual assistant. The system100may determine which virtual assistant is to handle the utterance, and process the utterance accordingly; for example, by sending data representing the utterance to the particular speech processing system corresponding to the virtual assistant as illustrated inFIG.5and/or processing the utterance using a configuration corresponding to the virtual assistant as illustrated inFIG.6.

The system100may be configured with a default virtual assistant such that if the user utters a command or request without a wakeword, the system100will determine that the utterance should be handled by the default virtual assistant. For example, the device110may include a button113that may activate the microphone114and indicate to the system100to begin receiving audio11and processing the resulting audio data. When the user selects the button113, the microphone114may receive audio11, and the remote control105may send voice data115to the device110. In some implementations, however, the button113and/or microphone114may be on or otherwise physically integrated with the device110itself. The system100may process the audio data to determine whether the audio data includes representations of one or more wakewords. If the system100does not detect any known wakewords in the audio data, the system100may determine that the utterance is intended for the default virtual assistant.

The time between a user speaking a command to the system100and receiving a response from the system100may be referred to as the user-perceived latency. User-perceived latency is an important system metric because it relates directly to a user's experience. That is, a user experiencing high latency may become frustrated and reduce their use of the system100. Thus, any steps that can be taken to reduce the user-perceived latency will improve the user experience and increase use.

In order to reduce the user-perceived latency, the system100may attempt to quickly determine whether or not a user has spoken a wakeword. For example, an utterance that begins with a wakeword may be 5 seconds long, while the wakeword may be spoken at the beginning of the utterance, perhaps in the first second. Thus, if the system100determines after the first second that no wakeword is spoken, it may begin processing the audio data consistent with the default virtual assistant. In contrast, if the system100processes audio data for the entire utterance before determining that the utterance is intended for the default virtual assistant, the user-perceived latency may be four second longer. Therefore, the system100may include features for determining early in the utterance whether or not a wakeword has been spoken, and promptly sending the audio data (e.g., in a streaming fashion) for speech processing corresponding to the default or indicated virtual assistant.

FIG.1Aillustrates various components of the system100that may be configured to make an early determination regarding the presence of one or more wakewords in a received utterance. The various components may be software, logic on separate chips, separate dies on a system on chip (SoC), etc. The components may be embodied in a single executable where an individual component may be process or subroutine of the executable. Boxes abutting each other in the drawings (e.g., the client software130and the multi-assistant middleware140) indicate that the components represented by the boxes may interface directly. Arrows may indicate directions of signal and/or data flow.

The system100may include an acoustic front end (AFE)121that may receive the voice data115from the microphone114and generate audio data111for processing by downstream components (e.g., ASR, NLU, etc.). The AFE121may also provide a representation of the voice data115to one or more wakeword detectors122and/or a voice activity detector (VAD)124. The AFE121may include processing to filter the voice data115. For example, the AFE121may perform echo cancelation, noise suppression, beamforming, high- and/or low-pass filtering, etc. The AFE121may output both raw audio data and audio data processed using one or more of the aforementioned techniques. The AFE121may stream the audio data111to the wakeword detector(s)122, the VAD124, and/or a buffer134. The buffer134may be a memory or storage configured to store the audio data111unless or until the system100determines a virtual assistant for handling the audio data111.

The wakeword detector122can receive the audio data111from the AFE121and process it to detect the presence of one or more wakewords. In some implementations, the system100may include multiple wakeword detectors122(e.g., corresponding to different wakewords and/or virtual assistants). In some implementations, the system100may include a wakeword detector122configured to recognize multiple wakewords.

The wakeword detector122may be a hardware or software component. For example, the wakeword detector122may be executable code that may run without external knowledge of other components. The wakeword detector122may be a library that integrates with an application (e.g., the client software130), where the library contains a description or descriptions (e.g., model parameters) corresponding one or more wakewords. In some implementations, the system100may store model parameters and/or other information about wakewords in the wakeword storage123. The wakeword storage may be volatile, semi-volatile, or non-volatile memory configured to store and output wakeword representational data for the wakeword detector(s)122. The wakeword storage123may include other data and/or metadata for a wakeword, such as wakeword durations For example, the wakeword storage123may include timing information for a wakeword, such as a typical speaking length (e.g., 0.8 seconds, 1.1 seconds, etc.).

The wakeword detector122may, in some implementations, output a signal when a representation of a wakeword is detected; for example, the wakeword detector122may output a logic 0 when no wakeword is detected, transitioning to a logic 1 if/when a wakeword is detected. The wakeword detector122may output the wakeword detection signal to the client software130and/or other components of the system100. For example, in some implementations, the wakeword detector122may output a wakeword detection signal to the AFE121, in response to which the AFE121may begin generating and streaming the audio data111for processing by the system100. In some implementations, the wakeword detector122may output other metadata upon detecting a wakeword. The other metadata may include a confidence score associated with the detection, fingerprinting (e.g., whether the audio data111included a fingerprint signal on the portion representing the wakeword to indicate that the wakeword was output from a media device during, for example, a commercial or other mass media event), and/or other metrics.

The wakeword detector122of the device110may process the audio data111, representing the audio11, to determine whether speech is represented therein. The device110may use various techniques to determine whether the audio data111includes speech. In some examples, the device110may apply voice-activity detection (VAD) techniques. Such techniques may determine whether speech is present in audio data based on various quantitative aspects of the audio data, such as the spectral slope between one or more frames of the audio data; the energy levels of the audio data in one or more spectral bands; the signal-to-noise ratios of the audio data in one or more spectral bands; or other quantitative aspects. In other examples, the device110may implement a classifier configured to distinguish speech from background noise. The classifier may be implemented by techniques such as linear classifiers, support vector machines, and decision trees. In still other examples, the device110may apply hidden Markov model (HMM) or Gaussian mixture model (GMM) techniques to compare the audio data to one or more acoustic models in storage, which acoustic models may include models corresponding to speech, noise (e.g., environmental noise or background noise), or silence. Still other techniques may be used to determine whether speech is present in audio data.

Wakeword detection is typically performed without performing linguistic analysis, textual analysis, or semantic analysis. Instead, the audio data, representing the audio11, is analyzed to determine if specific characteristics of the audio data match preconfigured acoustic waveforms, audio signatures, or other data corresponding to a wakeword.

The VAD124may be a hardware and/or software component configured to detect the presence of voice-like audio in the audio data111. The VAD124may determine whether voice activity may be present in the audio data based on, for example, the frequency/energy content of the audio data. For example, the VAD124may output an indication of voice activity based on detecting energy in the range of 200-4,000 Hz. The output may be a high/low signal indicating that voice activity is or is not detected. In some implementations, the VAD124may be a part of the AFE121, the client software130, or a standalone hardware or software component. The VAD124may provide a VAD signal to the client software130and/or other components of the system100.

The VAD signal may be used to, for example, determine that a user has begun speaking; for example, following detection of a gesture such as a press of the button113. A user may press the button113and pause for some time before speaking. In some implementations, the system100can use the VAD124and the VAD signal it produces as a trigger to start the timer132(e.g., as an alternative or in addition to starting a timer when a button signal117is received). The client software130is configured to wait 1.2 s before defaulting to the default virtual assistant if no known wakewords are detected, but a user does not begin speaking until 0.5 s after pressing the button113, the wakeword detector122may not have time to detect a wakeword longer than ˜0.7 s before the system100defaults to the default virtual assistant. The timer132may be configured with an added delay to allow for a delay between receiving the button signal117and the beginning of speech; however, the added delay may add to user-perceived latency.

In some implementations, however, the client software130may start the timer132upon detection of voice activity. For example, the user may press the button113, and the system100may begin generating audio data. After some time, perhaps 0.5 s, the user may begin speaking. The VAD124may detect the voice activity and send a resulting VAD signal to the client software130. The client software130may start the timer132. After a predetermined time (e.g., a threshold amount of time as measured following detection of voice activity), the client software130may determine whether the wakeword detector122has indicated a detection of a representation of a wakeword in the audio data111. For example, the threshold duration of time may be equal to a length of the longest wakeword configured for use on the device, plus a margin duration of time. The wakeword length may be a speaking length of the wakeword; for example, the TP95, which is the amount of time within which 95% utterances of the wakeword would fall. Typical TP95 values for various wakewords may range from, for example, 0.5 s to 2.0 s; however, most wakewords may fall within a range of 1 s+/−200 ms. The margin duration may allow for extra time for a user to begin speaking after a button press (e.g., 250-500 ms), or to allow for elongated pronunciations of the wakeword (e.g., 100-200 ms). The margin duration may be set proportionally to the wakeword duration (e.g., an additional 10%, 15%, 20% of time, etc.), or may be set as an absolute amount of time (e.g., 100, 200, 300 ms, etc.). The wakeword length and/or margin duration may be determined using example utterances received and/or stored by the system100.

If the client software130determines that the one or more wakewords are undetected during the threshold duration of time, the client software130may default to the default virtual assistant. In this manner, the system100can reduce the amount of user-perceived latency experienced by a user who begins speaking shortly after pressing the button113.FIG.3, described below, illustrates an example of early default virtual assistant determination using a timer started based on a VAD signal.

In some implementations, the system100may perform a more sophisticated analysis of the VAD signal. For example, speech may include silent moments between and within words. A VAD124may have a rapid response, on the order of 10 ms. Thus, a VAD signal corresponding to speech may include brief periods (e.g., on the order of tens or hundreds of milliseconds) where the VAD124may indicate no voice activity. Similarly, some wakewords may have a VAD signature that is continuous, while others may have gaps in the VAD signal where no voice activity is detected. The wakeword storage123may store VAD signatures for wakewords. The VAD124and/or the client software130may use the VAD signatures to determine that the VAD signal is inconsistent with wakewords enabled for the system100. For example, the VAD signatures for all enabled wakewords represent continuous voice activity detection for the duration of the wakeword, and the wakewords may all have a duration of between 1 s and 1.25 s. Thus, if the VAD124detects a gap in voice activity after, for example, 200 or 300 ms of detected voice activity, the system100may determine that the gap is inconsistent with enabled wakewords, and make an early determination (e.g., before the timer132has elapsed) that the user has not spoken a wakeword at the beginning of speech. The system100may thus determine that the utterance is intended for the default virtual assistant.FIG.4, described below, illustrates an example of early default virtual assistant determination based on VAD signals.

In some implementations, the VAD signatures may include additional description of VAD signal gaps corresponding to a wakeword. For example, a wakeword may have a first segment having 300 ms+/−60 ms of voice activity detected, followed by a second segment having 100 ms+/−20 ms of no voice activity detected, followed by a third segment having 400 ms+/−160 ms of voice activity detected. Another wakeword may have additional segments of voice activity/no voice activity described. The system100may, using such VAD signatures, make an early determination that voice activity detected/no voice activity detected segments do not match enabled wakewords, and thus determine that the utterance is intended for the default virtual assistant. In some implementations, the client software130or other component may be configured with a trained model, such as a recurrent neural network or classifier, configured to predict whether a VAD signal from the VAD124matches enabled wakewords. Such a model may be trained using labeled sample data comprised of speech with and without wakewords.

The device110(and/or other components of the system100) may execute client software130. The client software130may receive detection signals from the wakeword detector(s)122and/or VAD124. The client software130may also receive audio data111from the AFE121. The client software130may include or interface with a timer132. The client software130may use the timer132to determine when to default to a default virtual assistant; for example, in absence of a wakeword signal received prior to a threshold duration of time elapsing. The client software130may control a buffer134configured to store audio data111from the AFE121, and send the audio data from the buffer134, via the multi-assistant middleware140to a voice service corresponding to a virtual assistant selected to process the utterance. An example operation of the client software130and the system100is described in additional detail below with reference toFIG.1B.

The multi-assistant middleware140may include software and/or logic configured to control the invocation of the virtual assistants and enforce rules to ensure proper receipt and transmission of user data. For example, the multi-assistant middleware140may control the creation of a dialog instance (e.g., a session) between a user and a virtual assistant. A dialog instance may be a singleton, meaning that the system100may only have a single instance active at a given time. In other words, a user may only be in contact with one virtual assistant at a time. Thus, data transferred between the user (e.g., the user's device110) and a first virtual assistant is inaccessible to other virtual assistants.

When the client software130receives the button signal117(or other gesture), the client software130may begin receiving audio data111and storing it in the buffer134. Upon determining a virtual assistant for handling the utterance, the client software130may send a request to the multi-assistant middleware140to initiate a dialog between the user and the determined virtual assistant. The multi-assistant middleware140may deny the request if there is an existing active dialog (e.g., another virtual assistant is listening or speaking via the device110). If the multi-assistant middleware140grants the request, it may provide a dialog identifier to the client software130. The client software130may use the dialog identifier to cause the multi-assistant middleware140to allow transfer of audio data from the buffer134to the determined virtual assistant. Thus, in absence of the multi-assistant middleware140granting the dialog request and providing the dialog identifier, the client software130may be unable to transmit audio data to a virtual assistant (or anywhere else).

The functions of a virtual assistant may be accessed via an assistant voice service160. The assistant1voice service160aand the assistant2voice service160b(collectively, “assistant voice services160”) may correspond to a first virtual assistant and a second virtual assistant, respectively. An assistant voice service160may represent language processing components, skill components, language output components, and/or associated components as described in further detail below with reference toFIGS.5and6. In various implementations, the components represented by the assistant voice services160may reside on the device110or the system120, or divided or shared between the two. In some implementations, the assistant1voice service160amay correspond to one or more components of a first system120aand the assistant2voice service160bmay correspond to one or more components of a second system120b, etc. In some implementations, multiple assistant voice services160may correspond to a single system120, where configuration for each virtual assistant are stored in a multi-assistant component and used to process a user command and/or request using settings and/or resources corresponding to the determined virtual assistant.

The multi-assistant middleware140may, when a dialog request is granted, send audio data to the determined assistant voice service160via an assistant component150aor150b(collectively, “assistant components150”). For example, the multi-assistant middleware140may send and receive data to and from the assistant1voice service160avia an assistant1component150a. The multi-assistant middleware140may send and receive data to and from the assistant2voice service160bvia an assistant2component150b. In some cases, the multi-assistant middleware140may interface with an assistant component150via an assistant interface145. The assistant interface145may translate data transferred between the multi-assistant middleware140and the assistant component150. For example, the system100may include any number of assistant components150communicating with respective assistant voice services160. In some cases, an assistant voice service160and assistant component150may be configured to interface with the multi-assistant middleware140directly by using a shared communication protocol, APIs, or other means of exchanging data. In some cases, however, the system100may include an assistant interface145to facilitate integration of, for example, assistant component and voice services corresponding to third-party virtual assistants developed with/for different communication protocols.

FIG.1Bis flowchart illustrating an example method101of default assistant fallback in the multi-assistant system100, according to embodiments of the present disclosure. The method101may include offline stages102(e.g., that may be performed prior to receiving an indication to begin capturing audio) as well as runtime stages103(e.g., that may be performed in response to receiving an indication to begin capturing audio and/or as part of processing the captured audio). The method101may be performed using various components of the system100described herein; for example, with reference toFIGS.1A,1B,5, and6, etc.

The method101may include determining (180) a first wakeword speaking duration; that is, an expected speaking length of a first wakeword, such as “Alexa.” The first wakeword speaking duration may be, for example, 0.8. The wakeword duration may correspond to, for example, a time in which 90% (or 95%, 99%, etc.) of speakers say the wakeword. The method101may include determining (182) a second wakeword speaking duration. The second wakeword may be, for example, “Hi, Computer,” and may have a speaking duration of 1.1 s. In some implementations, the method101may include determining speaking durations for additional wakewords enabled for use with the system100. The method101may include determining (184) a longest speaking duration of all the enabled wakewords. In this example, the longest wakeword speaking duration may be 1.1 s. The method101may include adding (186) to the longest wakeword speaking duration a margin duration, such as an amount of time equal to 10%, 15%, 20%, etc. of the longest wakeword speaking duration. The total duration for use in the timing operations of the method101may thus be approximately 1.3 s. The offline stages102may be performed prior to and/or during the runtime stages. For example, in some implementations, the system100may calculate the total duration prior to receiving an indication to capture audio (e.g., at a stage188). In some implementations, the system100may calculate the total duration after receiving the indication to capture audio but before the determination to default, based on an elapsed time, to the default virtual assistant (e.g., at a decision block196).

The method101may include the runtime stages103, which may begin by receiving (188) an indication to capture audio. The indication may include a button press, voice activity detection, or other signal indicating to the system100to being capturing audio, generating audio data, and processing the audio data. The method101may include starting (190) a timer. In some implementations, the system100may start the timer upon receiving the indication to capture audio; for example, the button press. In some implementations, the system100may wait until voice activity is detected subsequent to the button press to begin start the timer. The system100may set the timer to run until the total duration calculated at the stage186has lapsed. While the timer is running, the system100may process the audio data to determine (at a decision block192) a potential representation of one or more wakewords (e.g., the wakewords whose durations were determined in the stages180and/or182). If the system100detects a wakeword (“yes” at192), the method101may include processing (194) the audio data according to the invoked virtual assistant; that is, the virtual assistant corresponding to the detected wakeword. The system100may send the audio data to one or more components associated with the determined virtual assistant for processing. If, however, the system100does not detect a wakeword (“no” at192), the method101may include determining (196) whether the timer has lapsed. If the timer has not lapsed (“no” at196), the method101will return to the decision block192and continue processing the audio data to detect a representation of a wakeword. If the timer has lapsed (“yes” at198), the method101may include processing the audio data according to a default virtual assistant.

The system100may then process the audio data according to the determined virtual assistant, and perform one or more actions responsive to the audio data.

FIG.2is a signal flow diagram illustrating example operations of a multi-assistant system100processing an utterance that includes a wakeword, according to embodiments of the present disclosure. In the example operations illustrated inFIG.2, the system100starts a timer upon detection of voice activity following a button press, and detects a wakeword prior to time out. The system100thus sends the audio data for processing consistent with the virtual assistant associated with the detected wakeword via the assistant1voice service160a(e.g., via the assistant1component150a, not pictured).

The operations may begin with a detection (202) of a press of the button113(or other gesture). In response to the button press, the microphone114may turn on (204) and begin capturing audio. The microphone114may remain on and capturing audio (206) until the button113is released. In some implementations, the microphone114may be turned off by downstream components of the system100in response to, for example, detecting an end of speech by the AFE121and/or ASR components. The microphone114may be a passive electronic component, thus turning the microphone114on or off may be accomplished by connecting/disconnecting the microphone114from the AFE121, turning the AFE121(or component thereof such as a microphone preamplifier) on or off, controlling whether the AFE121is generating audio data representing captured audio, etc. When the microphone114is activated, the AFE121may receive (208) an analog audio signal from the microphone and convert it to audio data, which it may in turn send (210) in parallel to the wakeword detector122, the VAD124, and/or the buffer134. In this example, the AFE121has received an audio signal representing the word “Alexa.”

The VAD124may receive the audio data and may begin detecting voice activity in the audio and send (212) a VAD signal indicating that it has begun detecting speech. The client software130may receive the VAD signal and start (214) a timer. The timer may have been preconfigured for a threshold duration equal to a longest speaking duration of wakewords enabled for the device110/system100plus a margin duration. The timer may continue to run (216) for the threshold duration.

The wakeword detector122may receive the audio data and process it to determine whether the audio data includes a potential representation of any of the enabled wakewords. The system100may include one or more wakeword detectors122, and a wakeword detector may be configured to detect one or more wakewords. In the example shown inFIG.2, the wakeword detector122detects the wakeword “Alexa,” and sends (218) a wakeword detection signal. The client software130may receive the wakeword detection signal (e.g., in this example, prior to timeout of the timer), and determine (220) that the utterance is to be processed using an assistant voice service160corresponding to the detected wakeword; in this case, “Alexa” may correspond to the assistant1voice service160a. In response to determining that the utterance is to be processed using the assistant1voice service160a, the client software130may send (224) a dialog request to the multi-assistant middleware140. In this example, the multi-assistant middleware140may grant (226) the request, and update the system100state to a “listening” state (228). While the system100is in a listening state, the multi-assistant middleware140may reject other dialog requests. Upon granting the request, the multi-assistant middleware140may send (230) an indication representing a dialog handle back to the client software130. The dialog handle may include data such as a dialog identifier, a virtual assistant identifier, etc., that the client software130may use to request speech processing of the audio data using the assistant1voice service160a. The client software130may cause (238) the buffer134to send (240) the audio data stored therein to the multi-assistant middleware140, which may send (242) the audio data to the assistant1voice service160a. The assistant1voice service160amay (e.g., using the language processing components described with reference toFIGS.5and6) process (244) the audio data; for example, using ASR, NLU, one or more skills, and/or TTS to perform an action indicated by the utterance including generating a response.

The microphone114may keep receiving audio and sending (232) the audio signal to the AFE121. The AFE121may process the audio signal and generate audio data, which it may send (234) to the wakeword detector122, VAD124, and buffer134. The VAD124may have indicated (222) that no voice activity was detected for a time following “Alexa”; for example, due to a pause in the speech between the wakeword and the request. Upon receipt of the audio data representing the speech (“What time is it?”), the VAD124may again detect voice activity and send (236) a VAD signal indicating the detection. Following the speech, but before the stop capture command (254), the VAD124may determine (248) that it is no longer detecting voice activity. The system100may detect (250) the button release and deactivate (252) the microphone (e.g., the AFE121may no longer generate audio data and/or send it to other components). After the threshold duration has elapsed, the client software130may determine (246) that the timer has timed out.

The assistant1voice service160amay continue processing (244) the audio data to, among other things, determine an end of speech. The assistant1voice service160amay determine the end of speech based on a certain amount of time elapsed following recognizable speech in the audio data, based on a prediction made by a trained model (e.g., such as an ASR model), and/or based on an indication that the button113has been released (e.g., at the stage250). Upon determining an end of speech, the assistant1voice service160amay send (254) a stop capture command to the multi-assistant middleware140. The multi-assistant middleware140may, in response to the stop capture command, update (256) a state of the system100from a “listening” state to a “processing” or “thinking” state. In some implementations, the multi-assistant middleware140may, in response to the stop capture command, send a command to the AFE121to cease capturing audio and/or generating audio data.

The assistant1voice service160amay generate a response (e.g., “3 pm”) to the utterance and send (258) the response to the multi-assistant middleware140. The multi-assistant middleware140may update (260) the system state to “responding” or “speaking,” and send (262) the response data to the client software130. The client software130may cause (264) the device110to output the response from the speaker112in the form of synthesized speech representing an answer, confirmation, and/or other response to the utterance.

FIG.3is a signal flow diagram illustrating first example operations of default agent fallback in a multi-assistant system100processing an utterance that does not include a wakeword, according to embodiments of the present disclosure. In the example operations illustrated inFIG.3, the system100starts a timer upon detection of voice activity following a button press, but does not detect a wakeword prior to time out. The system100thus sends the audio data for processing consistent with the default virtual assistant via the assistant2voice service160b(e.g., via the assistant2interface145and the assistant2component150b, not pictured).

The operations may begin with a detection (302) of a press of the button113. In response to the button press, the microphone114may turn on (304) and begin capturing audio. The microphone114may remain on and capturing audio (306) until the button113is released. When the microphone114is activated, the AFE121may receive (308) an analog audio signal from the microphone and convert it to audio data, which it may in turn send (310) in parallel to the wakeword detector122, the VAD124, and/or the buffer134. In this example, the AFE121has received an audio signal representing the command “Play My Show.”

The VAD124may receive the audio data and may begin detecting voice activity in the audio and send (312) a VAD signal indicating that it has begun detecting speech. The client software130may receive the VAD signal and start (314) a timer. The timer may have been preconfigured for a threshold duration equal to a longest speaking duration of wakewords enabled for the device110/system100plus a margin duration. The timer may continue to run (316) for the threshold duration.

The wakeword detector122may receive the audio data and process it to determine whether the audio data includes a representation of any of the enabled wakewords. In the example shown inFIG.3, the wakeword detector122does not detect any wakewords prior to the timer timing out (324). In response to determining that no wakeword was detected prior to the timer timing out, the client software130may determine (326) that the utterance is to be processed using an assistant voice service160corresponding to a default virtual assistant for the system100, in this case the assistant2voice service160b.

The client software130may thus send (328) a dialog request to the multi-assistant middleware140. In this example, the multi-assistant middleware140may grant (330) the request, and update the system100state to a “listening” state (332). Upon granting the request, the multi-assistant middleware140may send (334) an indication representing a dialog handle back to the client software130. The dialog handle may include data such as a dialog identifier, a virtual assistant identifier, etc., that the client software130may use to request speech processing of the audio data using the assistant2voice service160b. The client software130may cause (338) the buffer134to send (340) the audio data stored therein to the multi-assistant middleware140, which may send (342) the audio data to the assistant2voice service160b. The assistant2voice service160bmay (e.g., using the language processing components described with reference toFIGS.5and6) process (344) the audio data; for example, using ASR, NLU, one or more skills, and/or TTS to perform an action indicated by the utterance including generating a response.

Following the speech, the VAD124may determine (318) that it is no longer detecting voice activity. The system100may detect (320) the button release and deactivate (322) audio capture.

The assistant2voice service160bmay continue processing (344) the audio data to, among other things, determine an end of speech. The assistant2voice service160bmay determine the end of speech. Upon determining an end of speech, the assistant2voice service160bmay send (354) a stop capture command to the multi-assistant middleware140. The multi-assistant middleware140may, in response to the stop capture command, update (356) a state of the system100from a “listening” state to a “processing” or “thinking” state.

The assistant2voice service160bmay generate a response (e.g., “Playing My Show.”) to the utterance and send (358) the response to the multi-assistant middleware140. The multi-assistant middleware140may update (360) the system state to “responding” or “speaking,” and send (362) the response data to the client software130. The client software130may cause (364) the device110to output the response from the speaker112in the form of synthesized speech representing an answer, confirmation, and/or other response to the utterance.

FIG.4is a signal flow diagram illustrating second example operations of default agent fallback in a multi-assistant system100processing an utterance that does not include a wakeword, according to embodiments of the present disclosure. In the example operations illustrated inFIG.4, the system100starts a timer upon detection of voice activity following a button press, but detects a gap in the VAD signal inconsistent with wakewords enabled for the device110/system100. The system100thus sends the audio data for processing consistent with the default virtual assistant via the assistant2voice service160b(e.g., via the assistant2interface145and the assistant2component150b, not pictured).

The operations may begin with a detection (402) of a press of the button113. In response to the button press, the microphone114may turn on (404) and begin capturing audio. The microphone114may remain on and capturing audio (406) until the button113is released. When the microphone114is activated, the AFE121may receive (408) an analog audio signal from the microphone and convert it to audio data, which it may in turn send (410) in parallel to the wakeword detector122, the VAD124, and/or the buffer134. In this example, the AFE121has received an audio signal representing a beginning of the command “Play.”

The VAD124may receive the audio data and may begin detecting voice activity in the audio and send (412) a VAD signal indicating that it has begun detecting speech. The client software130may receive the VAD signal and start (414) a timer. The timer may have been preconfigured for a threshold duration equal to a longest speaking duration of wakewords enabled for the device110/system100plus a margin duration. The timer may continue to run (416) for the threshold duration.

The wakeword detector122may receive the audio data and process it to determine whether the audio data includes a representation of any of the enabled wakewords. In the example shown inFIG.4, the wakeword detector122does not detect any wakewords prior to the timer timing out (424). However, even before the timeout, the VAD124may indicate a gap in voice activity inconsistent with wakewords enabled for the device. For example, the microphone114may further receive audio representing “Trivia” and send (420) the audio signal to the AFE121. The AFE121may send (422) audio data representing “Trivia” to the wakeword detector122, the VAD124, and/or the buffer134. The VAD124may determine a gap in voice activity between “Play” and “Trivia.” After indicating detected voice activity at the stage412, the VAD124may indicate no voice activity at a stage418. Subsequently, the VAD124may indicate voice activity at a stage424, and then no voice activity at a stage425. The system100may determine that the gap indicated by the VAD signal between the stages418and424is inconsistent with the VAD signatures of enabled wakewords. As a result, the client software130may determine (426) that the utterance is to be processed using an assistant voice service160corresponding to a default virtual assistant for the system100, in this case the assistant2voice service160b.

The client software130may thus send (428) a dialog request to the multi-assistant middleware140. In this example, the multi-assistant middleware140may grant (430) the request, and update the system100state to a “listening” state (432). Upon granting the request, the multi-assistant middleware140may send (434) an indication representing a dialog handle back to the client software130. The dialog handle may include data such as a dialog identifier, a virtual assistant identifier, etc., that the client software130may use to request speech processing of the audio data using the assistant2voice service160b. The client software130may cause (438) the buffer134to send (440) the audio data stored therein to the multi-assistant middleware140, which may send (442) the audio data to the assistant2voice service160b. The assistant2voice service160bmay (e.g., using the language processing components described with reference toFIGS.5and6) process (444) the audio data; for example, using ASR, NLU, one or more skills, and/or TTS to perform an action indicated by the utterance including generating a response.

Following the speech, the system100may detect (446) the button release and deactivate (448) audio capture.

The assistant2voice service160bmay continue processing (444) the audio data to, among other things, determine an end of speech. The assistant2voice service160bmay determine the end of speech. Upon determining an end of speech, the assistant2voice service160bmay send (454) a stop capture command to the multi-assistant middleware140. The multi-assistant middleware140may, in response to the stop capture command, update (456) a state of the system100from a “listening” state to a “processing” or “thinking” state.

The assistant2voice service160bmay generate a response (e.g., “Welcome to Trivia.”) to the utterance and send (458) the response to the multi-assistant middleware140. The multi-assistant middleware140may update (460) the system state to “responding” or “speaking,” and send (462) the response data to the client software130. The client software130may cause (464) the device110to output the response from the speaker112in the form of synthesized speech representing an answer, confirmation, and/or other response to the utterance.

FIG.5is a conceptual diagram illustrating components that may be included in a first example implementation of the multi-assistant system100, according to embodiments of the present disclosure. In the implementation of the system100shown inFIG.5, components corresponding to a first virtual assistant (e.g., the assistant1voice service160a) may be divided and/or shared between the device110and a first system120a, components corresponding to a second virtual assistant may be divided and/or shared between the device110and a second system120b, etc. The respective systems120may be separate and distinct from each other. Data from the device110corresponding to a first virtual assistant (e.g., an utterance to be handled by the first virtual assistant) may be sent to the first system120a, and data from the device corresponding to a second virtual assistant may be sent to the second system120b, etc.

The system100may operate using various components as described inFIG.5. The various components may be located on same or different physical devices. Communication between various components may occur directly or across a network(s)199. The device110may include audio capture component(s), such as a microphone or array of microphones of a device110, captures audio11and creates corresponding audio data. Once speech is detected in audio data representing the audio11, the device110may determine if the speech is directed at the device110/system120. In at least some embodiments, such determination may be made using a wakeword detection component122. The wakeword detection component122may be configured to detect various wakewords. In at least some examples, each wakeword may correspond to a name of a different digital assistant. An example wakeword/digital assistant name is “Alexa.” In another example, input to the system may be in form of text data, for example as a result of a user typing an input into a user interface of device110. Other input forms may include indication that the user has pressed a physical or virtual button on device110, the user has made a gesture, etc.

Following detection of a wakeword, button press, or other indication to begin receiving input, the device110may “wake” and begin generating and processing audio data111representing the audio11. The audio data111may include data corresponding to the wakeword; in other embodiments, the portion of the audio corresponding to the wakeword may be removed prior to downstream processing of the audio data111(e.g., ASR and/or NLU). In the case of touch input detection or gesture based input detection, the audio data may not include a wakeword.

Upon receipt by the system100, the audio data111may be sent to an orchestrator component530. The orchestrator component530may include memory and logic that enables the orchestrator component530to transmit various pieces and forms of data to various components of the system, as well as perform other operations as described herein.

The orchestrator component530may send the audio data111to a language processing components592. The language processing components592(sometimes also referred to as a spoken language understanding (SLU) component) includes an automatic speech recognition (ASR) component550and a natural language understanding (NLU) component560. The ASR component550may transcribe the audio data111into text data. The ASR component550may receive the audio data111in a streaming fashion; that is, the ASR component550may begin receiving and/or processing the audio data111as it is generated by the system100and without necessarily waiting for the user to stop speaking, release the button113, or otherwise indicate an end of speech. The text data output by the ASR component550represents one or more than one (e.g., in the form of an N-best list) ASR hypotheses representing speech represented in the audio data111. The ASR component550interprets the speech in the audio data111based on a similarity between the audio data111and pre-established language models. For example, the ASR component550may compare the audio data111with models for sounds (e.g., acoustic units such as phonemes, senons, phones, etc.) and sequences of sounds to identify words that match the sequence of sounds of the speech represented in the audio data111. The ASR component550sends the text data generated thereby to an NLU component560, via, in some embodiments, the orchestrator component530. The text data sent from the ASR component550to the NLU component560may include a single top-scoring ASR hypothesis or may include an N-best list including multiple top-scoring ASR hypotheses. An N-best list may additionally include a respective score associated with each ASR hypothesis represented therein. The ASR component550is described in greater detail below with regard toFIG.7.

The language processing components592may further include a NLU component560. The NLU component560may receive the text data from the ASR component. The NLU component560may attempts to make a semantic interpretation of the phrase(s) or statement(s) represented in the text data input therein by determining one or more meanings associated with the phrase(s) or statement(s) represented in the text data. The NLU component560may determine an intent representing an action that a user desires be performed and may determine information that allows a device (e.g., the device110, the system(s)120, a skill component590, a skill system(s)525, etc.) to execute the intent. For example, if the text data corresponds to “play the 5thSymphony by Beethoven,” the NLU component560may determine an intent that the system output music and may identify “Beethoven” as an artist/composer and “5th Symphony” as the piece of music to be played. For further example, if the text data corresponds to “what is the weather,” the NLU component560may determine an intent that the system output weather information associated with a geographic location of the device110. In another example, if the text data corresponds to “turn off the lights,” the NLU component560may determine an intent that the system turn off lights associated with the device110or the user5. However, if the NLU component560is unable to resolve the entity—for example, because the entity is referred to by anaphora such as “this song” or “my next appointment”—the language processing components592may send a decode request to another speech processing system for information regarding the entity mention and/or other context related to the utterance. The speech processing system592may augment, correct, or base results data upon the audio data111as well as any data received from the other speech processing system.

The NLU component560may return NLU results data985/925(which may include tagged text data, indicators of intent, etc.) back to the orchestrator component530. The orchestrator component530may forward the NLU results data to a skill component(s)590. If the NLU results data includes a single NLU hypothesis, the NLU component560and the orchestrator component530may direct the NLU results data to the skill component(s)590associated with the NLU hypothesis. If the NLU results data985/925includes an N-best list of NLU hypotheses, the NLU component560and the orchestrator component530may direct the top scoring NLU hypothesis to a skill component(s)590associated with the top scoring NLU hypothesis. The system may also include a post-NLU ranker565which may incorporate other information to rank potential interpretations determined by the NLU component560. The NLU component560, post-NLU ranker565and other components are described in greater detail below with regard toFIGS.8and9.

A skill component may be software running on the system(s)120that is akin to a software application. That is, a skill component590may enable the system(s)120to execute specific functionality in order to provide data or produce some other requested output. As used herein, a “skill component” may refer to software that may be placed on a machine or a virtual machine (e.g., software that may be launched in a virtual instance when called). A skill component may be software customized to perform one or more actions as indicated by a business entity, device manufacturer, user, etc. What is described herein as a skill component may be referred to using many different terms, such as an action, bot, app, or the like. The system(s)120may be configured with more than one skill component590. For example, a weather service skill component may enable the system(s)120to provide weather information, a car service skill component may enable the system(s)120to book a trip with respect to a taxi or ride sharing service, a restaurant skill component may enable the system(s)120to order a pizza with respect to the restaurant's online ordering system, etc. A skill component590may operate in conjunction between the system(s)120and other devices, such as the device110, in order to complete certain functions. Inputs to a skill component590may come from speech processing interactions or through other interactions or input sources. A skill component590may include hardware, software, firmware, or the like that may be dedicated to a particular skill component590or shared among different skill components590.

A skill support system(s)525may communicate with a skill component(s)590within the system(s)120and/or directly with the orchestrator component530or with other components. A skill support system(s)525may be configured to perform one or more actions. An ability to perform such action(s) may sometimes be referred to as a “skill.” That is, a skill may enable a skill support system(s)525to execute specific functionality in order to provide data or perform some other action requested by a user. For example, a weather service skill may enable a skill support system(s)525to provide weather information to the system(s)120, a car service skill may enable a skill support system(s)525to book a trip with respect to a taxi or ride sharing service, an order pizza skill may enable a skill support system(s)525to order a pizza with respect to a restaurant's online ordering system, etc. Additional types of skills include home automation skills (e.g., skills that enable a user to control home devices such as lights, door locks, cameras, thermostats, etc.), entertainment device skills (e.g., skills that enable a user to control entertainment devices such as smart televisions), video skills, flash briefing skills, as well as custom skills that are not associated with any pre-configured type of skill.

The system(s)120may be configured with a skill component590dedicated to interacting with the skill support system(s)525. Unless expressly stated otherwise, reference to a skill, skill device, or skill component may include a skill component590operated by the system(s)120and/or skill operated by the skill support system(s)525. Moreover, the functionality described herein as a skill or skill may be referred to using many different terms, such as an action, bot, app, or the like. The skill590and or skill support system(s)525may return output data to the orchestrator component530.

The system120includes a language output components593. The language output components593includes a natural language generation (NLG) component579and a text-to-speech (TTS) component580. The NLG component579can generate text for purposes of TTS output to a user. For example the NLG component579may generate text corresponding to instructions corresponding to a particular action for the user to perform. The NLG component579may generate appropriate text for various outputs as described herein. The NLG component579may include one or more trained models configured to output text appropriate for a particular input. The text output by the NLG component579may become input for the TTS component580(e.g., output text data1010discussed below). Alternatively or in addition, the TTS component580may receive text data from a skill590or other system component for output.

The NLG component579may include a trained model. The NLG component579generates text data1010from dialog data received (e.g., by a dialog manager) such that the output text data1010has a natural feel and, in some embodiments, includes words and/or phrases specifically formatted for a requesting individual. The NLG may use templates to formulate responses. And/or the NLG system may include models trained from the various templates for forming the output text data1010. For example, the NLG system may analyze transcripts of local news programs, television shows, sporting events, or any other media program to obtain common components of a relevant language and/or region. As one illustrative example, the NLG system may analyze a transcription of a regional sports program to determine commonly used words or phrases for describing scores or other sporting news for a particular region. The NLG may further receive, as inputs, a dialog history, an indicator of a level of formality, and/or a command history or other user history such as the dialog history.

The NLG system may generate dialog data based on one or more response templates. Further continuing the example above, the NLG system may select a template in response to the question, “What is the weather currently like?” of the form: “The weather currently is $weather_information$.” The NLG system may analyze the logical form of the template to produce one or more textual responses including markups and annotations to familiarize the response that is generated. In some embodiments, the NLG system may determine which response is the most appropriate response to be selected. The selection may, therefore, be based on past responses, past questions, a level of formality, and/or any other feature, or any other combination thereof. Responsive audio data representing the response generated by the NLG system may then be generated using the text-to-speech component580.

The TTS component580may generate audio data (e.g., synthesized speech) from text data using one or more different methods. Text data input to the TTS component580may come from a skill component590, the orchestrator component530, or another component of the system. In one method of synthesis called unit selection, the TTS component580matches text data against a database of recorded speech. The TTS component580selects matching units of recorded speech and concatenates the units together to form audio data. In another method of synthesis called parametric synthesis, the TTS component580varies parameters such as frequency, volume, and noise to create audio data including an artificial speech waveform. Parametric synthesis uses a computerized voice generator, sometimes called a vocoder.

The system100(either on device110, system120, or a combination thereof) may include profile storage for storing a variety of information related to individual users, groups of users, devices, etc. that interact with the system. As used herein, a “profile” refers to a set of data associated with a user, group of users, device, etc. The data of a profile may include preferences specific to the user, device, etc.; input and output capabilities of the device; internet connectivity information; user bibliographic information; subscription information, as well as other information.

The profile storage570may include one or more user profiles, with each user profile being associated with a different user identifier/user profile identifier. Each user profile may include various user identifying data. Each user profile may also include data corresponding to preferences of the user. Each user profile may also include preferences of the user and/or one or more device identifiers, representing one or more devices of the user. For instance, the user account may include one or more IP addresses, MAC addresses, and/or device identifiers, such as a serial number, of each additional electronic device associated with the identified user account. When a user logs into to an application installed on a device110, the user profile (associated with the presented login information) may be updated to include information about the device110, for example with an indication that the device is currently in use. Each user profile may include identifiers of skills that the user has enabled. When a user enables a skill, the user is providing the system120with permission to allow the skill to execute with respect to the user's natural language user inputs. If a user does not enable a skill, the system120may not invoke the skill to execute with respect to the user's natural language user inputs.

The profile storage570may include one or more group profiles. Each group profile may be associated with a different group identifier. A group profile may be specific to a group of users. That is, a group profile may be associated with two or more individual user profiles. For example, a group profile may be a household profile that is associated with user profiles associated with multiple users of a single household. A group profile may include preferences shared by all the user profiles associated therewith. Each user profile associated with a group profile may additionally include preferences specific to the user associated therewith. That is, each user profile may include preferences unique from one or more other user profiles associated with the same group profile. A user profile may be a stand-alone profile or may be associated with a group profile.

The profile storage570may include one or more device profiles. Each device profile may be associated with a different device identifier. Each device profile may include various device identifying information. Each device profile may also include one or more user identifiers, representing one or more users associated with the device. For example, a household device's profile may include the user identifiers of users of the household.

FIG.6is a conceptual diagram illustrating components that may be included in a second example implementation of the multi-assistant system100, according to embodiments of the present disclosure. In the implementation of the system100shown inFIG.6, components corresponding to multiple virtual assistant may be divided and/or shared between the device110and the system120. Processing of data by components of the system120may be performed according to a determined virtual assistant using configuration data maintained by the multi-assistant component615. For example, data received by the system120for processing may include an assistant identifier or other metadata that the components of the system120may use to process the data in a manner consistent with the determined virtual assistant. Thus, the system120may perform language processing using models, entity libraries, etc. corresponding to the determined virtual assistant, and may generate synthesized speech using voice parameters corresponding to the determined virtual assistant, etc.

In some implementations, the system120may have and/or interface with skill components dedicated to a particular virtual assistant. For example, the device110and/or system120may include first assistant skills690, which may include skill components590as previously described. The device110and/or system120may further include second assistant skills691including skill components690a,690b, and690c(collectively, “skill components690”). In some implementations, the skill components690may be the same as or similar to the skill components590. In some implementations, the first assistant skills690may provide different functionality than the second assistant skills. In some implementations, the first assistant skills690may be general-purpose skills (e.g., shopping, weather, music, etc.) not specific to a particular domain or device, while the second assistant skills691may be specialized to a certain domain, device, service, etc., such as for controlling operations of the smart TV110g. Some of the skill components590may interface with one or more skill support systems525; similarly, some of the skill components690may interface with one or more skill support systems625. In some implementations, the system100may have other skills and/or skill components that are common to more than one virtual assistant.

Other components of the system100illustrated inFIG.6, such as the language processing components592, language output components593, profile storage570, and/or orchestrator component530may be the same as or similar to the corresponding components as described with respect toFIG.5.

FIG.7is a conceptual diagram of an ASR component550, according to embodiments of the present disclosure. The ASR component550may interpret a spoken natural language input based on the similarity between the spoken natural language input and pre-established language models754stored in an ASR model storage752. For example, the ASR component550may compare the audio data with models for sounds (e.g., subword units or phonemes) and sequences of sounds to identify words that match the sequence of sounds spoken in the natural language input. Alternatively, the ASR component550may use a finite state transducer (FST)755to implement the language model functions.

When the ASR component550generates more than one ASR hypothesis for a single spoken natural language input, each ASR hypothesis may be assigned a score (e.g., probability score, confidence score, etc.) representing a likelihood that the corresponding ASR hypothesis matches the spoken natural language input (e.g., representing a likelihood that a particular set of words matches those spoken in the natural language input). The score may be based on a number of factors including, for example, the similarity of the sound in the spoken natural language input to models for language sounds (e.g., an acoustic model753stored in the ASR model storage752), and the likelihood that a particular word, which matches the sounds, would be included in the sentence at the specific location (e.g., using a language or grammar model754). Based on the considered factors and the assigned confidence score, the ASR component550may output an ASR hypothesis that most likely matches the spoken natural language input, or may output multiple ASR hypotheses in the form of a lattice or an N-best list, with each ASR hypothesis corresponding to a respective score.

The ASR component550may include a speech recognition engine758. The ASR component550receives audio data111(for example, received from a local device110having processed audio detected by a microphone by an acoustic front end (AFE) or other component). The speech recognition engine758compares the audio data111with acoustic models753, language models754, FST(s)755, and/or other data models and information for recognizing the speech conveyed in the audio data. The audio data111may be audio data that has been digitized (for example by an AFE) into frames representing time intervals for which the AFE determines a number of values, called features, representing the qualities of the audio data, along with a set of those values, called a feature vector, representing the features/qualities of the audio data within the frame. In at least some embodiments, audio frames may be 10 ms each. Many different features may be determined, as known in the art, and each feature may represent some quality of the audio that may be useful for ASR processing. A number of approaches may be used by an AFE to process the audio data, such as mel-frequency cepstral coefficients (MFCCs), perceptual linear predictive (PLP) techniques, neural network feature vector techniques, linear discriminant analysis, semi-tied covariance matrices, or other approaches known to those of skill in the art.

The speech recognition engine758may process the audio data111with reference to information stored in the ASR model storage752. Feature vectors of the audio data111may arrive at the system120encoded, in which case they may be decoded prior to processing by the speech recognition engine758.

The speech recognition engine758attempts to match received feature vectors to language acoustic units (e.g., phonemes) and words as known in the stored acoustic models753, language models 5B54, and FST(s)755. For example, audio data111may be processed by one or more acoustic model(s)753to determine acoustic unit data. The acoustic unit data may include indicators of acoustic units detected in the audio data111by the ASR component550. For example, acoustic units can consist of one or more of phonemes, diaphonemes, tonemes, phones, diphones, triphones, or the like. The acoustic unit data can be represented using one or a series of symbols from a phonetic alphabet such as the X-SAMPA, the International Phonetic Alphabet, or Initial Teaching Alphabet (ITA) phonetic alphabets. In some implementations a phoneme representation of the audio data can be analyzed using an n-gram based tokenizer. An entity, or a slot representing one or more entities, can be represented by a series of n-grams.

The acoustic unit data may be processed using the language model754(and/or using FST755) to determine ASR output data710. The ASR output data710can include one or more hypotheses. One or more of the hypotheses represented in the ASR output data710may then be sent to further components (such as the NLU component560) for further processing as discussed herein. The ASR output data710may include representations of text of an utterance, such as words, subword units, or the like.

The speech recognition engine758computes scores for the feature vectors based on acoustic information and language information. The acoustic information (such as identifiers for acoustic units and/or corresponding scores) is used to calculate an acoustic score representing a likelihood that the intended sound represented by a group of feature vectors matches a language phoneme. The language information is used to adjust the acoustic score by considering what sounds and/or words are used in context with each other, thereby improving the likelihood that the ASR component550will output ASR hypotheses that make sense grammatically. The specific models used may be general models or may be models corresponding to a particular domain, such as music, banking, etc.

The speech recognition engine758may use a number of techniques to match feature vectors to phonemes, for example using Hidden Markov Models (HMMs) to determine probabilities that feature vectors may match phonemes. Sounds received may be represented as paths between states of the HMM and multiple paths may represent multiple possible text matches for the same sound. Further techniques, such as using FSTs, may also be used.

The speech recognition engine758may use the acoustic model(s)753to attempt to match received audio feature vectors to words or subword acoustic units. An acoustic unit may be a senone, phoneme, phoneme in context, syllable, part of a syllable, syllable in context, or any other such portion of a word. The speech recognition engine758computes recognition scores for the feature vectors based on acoustic information and language information. The acoustic information is used to calculate an acoustic score representing a likelihood that the intended sound represented by a group of feature vectors match a subword unit. The language information is used to adjust the acoustic score by considering what sounds and/or words are used in context with each other, thereby improving the likelihood that the ASR component550outputs ASR hypotheses that make sense grammatically.

The speech recognition engine758may use a number of techniques to match feature vectors to phonemes or other acoustic units, such as diphones, triphones, etc. One common technique is using Hidden Markov Models (HMMs). HMMs are used to determine probabilities that feature vectors may match phonemes. Using HMMs, a number of states are presented, in which the states together represent a potential phoneme (or other acoustic unit, such as a triphone) and each state is associated with a model, such as a Gaussian mixture model or a deep belief network. Transitions between states may also have an associated probability, representing a likelihood that a current state may be reached from a previous state. Sounds received may be represented as paths between states of the HMM and multiple paths may represent multiple possible text matches for the same sound. Each phoneme may be represented by multiple potential states corresponding to different known pronunciations of the phonemes and their parts (such as the beginning, middle, and end of a spoken language sound). An initial determination of a probability of a potential phoneme may be associated with one state. As new feature vectors are processed by the speech recognition engine758, the state may change or stay the same, based on the processing of the new feature vectors. A Viterbi algorithm may be used to find the most likely sequence of states based on the processed feature vectors.

The probable phonemes and related states/state transitions, for example HMM states, may be formed into paths traversing a lattice of potential phonemes. Each path represents a progression of phonemes that potentially match the audio data represented by the feature vectors. One path may overlap with one or more other paths depending on the recognition scores calculated for each phoneme. Certain probabilities are associated with each transition from state to state. A cumulative path score may also be calculated for each path. This process of determining scores based on the feature vectors may be called acoustic modeling. When combining scores as part of the ASR processing, scores may be multiplied together (or combined in other ways) to reach a desired combined score or probabilities may be converted to the log domain and added to assist processing.

The speech recognition engine758may also compute scores of branches of the paths based on language models or grammars. Language modeling involves determining scores for what words are likely to be used together to form coherent words and sentences. Application of a language model may improve the likelihood that the ASR component550correctly interprets the speech contained in the audio data. For example, for an input audio sounding like “hello,” acoustic model processing that returns the potential phoneme paths of “H E L O”, “H A L O”, and “Y E L O” may be adjusted by a language model to adjust the recognition scores of “H E L O” (interpreted as the word “hello”), “H A L O” (interpreted as the word “halo”), and “Y E L O” (interpreted as the word “yellow”) based on the language context of each word within the spoken utterance.

FIGS.8and9illustrates how the NLU component560may perform NLU processing.FIG.8is a conceptual diagram of how natural language processing is performed, according to embodiments of the present disclosure. AndFIG.9is a conceptual diagram of how natural language processing is performed, according to embodiments of the present disclosure.

FIG.8illustrates how NLU processing is performed on text data. The NLU component560may process text data including several ASR hypotheses of a single user input. For example, if the ASR component550outputs text data including an n-best list of ASR hypotheses, the NLU component560may process the text data with respect to all (or a portion of) the ASR hypotheses represented therein.

The NLU component560may annotate text data by parsing and/or tagging the text data. For example, for the text data “tell me the weather for Seattle,” the NLU component560may tag “tell me the weather for Seattle” as an <OutputWeather> intent as well as separately tag “Seattle” as a location for the weather information.

The NLU component560may include a shortlister component850. The shortlister component850selects skills that may execute with respect to ASR output data710input to the NLU component560(e.g., applications that may execute with respect to the user input). The ASR output data710(which may also be referred to as ASR output data710) may include representations of text of an utterance, such as words, subword units, or the like. The shortlister component850thus limits downstream, more resource intensive NLU processes to being performed with respect to skills that may execute with respect to the user input.

Without a shortlister component850, the NLU component560may process ASR output data710input thereto with respect to every skill of the system, either in parallel, in series, or using some combination thereof. By implementing a shortlister component850, the NLU component560may process ASR output data710with respect to only the skills that may execute with respect to the user input. This reduces total compute power and latency attributed to NLU processing.

The shortlister component850may include one or more trained models. The model(s) may be trained to recognize various forms of user inputs that may be received by the system(s)120. For example, during a training period skill system(s)525associated with a skill may provide the system(s)120with training text data representing sample user inputs that may be provided by a user to invoke the skill. For example, for a ride sharing skill, a skill system(s)525associated with the ride sharing skill may provide the system(s)120with training text data including text corresponding to “get me a cab to [location],” “get me a ride to [location],” “book me a cab to [location],” “book me a ride to [location],” etc. The one or more trained models that will be used by the shortlister component850may be trained, using the training text data representing sample user inputs, to determine other potentially related user input structures that users may try to use to invoke the particular skill. During training, the system(s)120may solicit the skill system(s)525associated with the skill regarding whether the determined other user input structures are permissible, from the perspective of the skill system(s)525, to be used to invoke the skill. The alternate user input structures may be derived by one or more trained models during model training and/or may be based on user input structures provided by different skills. The skill system(s)525associated with a particular skill may also provide the system(s)120with training text data indicating grammar and annotations. The system(s)120may use the training text data representing the sample user inputs, the determined related user input(s), the grammar, and the annotations to train a model(s) that indicates when a user input is likely to be directed to/handled by a skill, based at least in part on the structure of the user input. Each trained model of the shortlister component850may be trained with respect to a different skill. Alternatively, the shortlister component850may use one trained model per domain, such as one trained model for skills associated with a weather domain, one trained model for skills associated with a ride sharing domain, etc.

The system(s)120may use the sample user inputs provided by a skill system(s)525, and related sample user inputs potentially determined during training, as binary examples to train a model associated with a skill associated with the skill system(s)525. The model associated with the particular skill may then be operated at runtime by the shortlister component850. For example, some sample user inputs may be positive examples (e.g., user inputs that may be used to invoke the skill). Other sample user inputs may be negative examples (e.g., user inputs that may not be used to invoke the skill).

As described above, the shortlister component850may include a different trained model for each skill of the system, a different trained model for each domain, or some other combination of trained model(s). For example, the shortlister component850may alternatively include a single model. The single model may include a portion trained with respect to characteristics (e.g., semantic characteristics) shared by all skills of the system. The single model may also include skill-specific portions, with each skill-specific portion being trained with respect to a specific skill of the system. Implementing a single model with skill-specific portions may result in less latency than implementing a different trained model for each skill because the single model with skill-specific portions limits the number of characteristics processed on a per skill level.

The portion trained with respect to characteristics shared by more than one skill may be clustered based on domain. For example, a first portion of the portion trained with respect to multiple skills may be trained with respect to weather domain skills, a second portion of the portion trained with respect to multiple skills may be trained with respect to music domain skills, a third portion of the portion trained with respect to multiple skills may be trained with respect to travel domain skills, etc.

Clustering may not be beneficial in every instance because it may cause the shortlister component850to output indications of only a portion of the skills that the ASR output data710may relate to. For example, a user input may correspond to “tell me about Tom Collins.” If the model is clustered based on domain, the shortlister component850may determine the user input corresponds to a recipe skill (e.g., a drink recipe) even though the user input may also correspond to an information skill (e.g., including information about a person named Tom Collins).

The NLU component560may include one or more recognizers863. In at least some embodiments, a recognizer863may be associated with a skill system525(e.g., the recognizer may be configured to interpret text data to correspond to the skill system525). In at least some other examples, a recognizer863may be associated with a domain such as smart home, video, music, weather, custom, etc. (e.g., the recognizer may be configured to interpret text data to correspond to the domain).

If the shortlister component850determines ASR output data710is potentially associated with multiple domains, the recognizers863associated with the domains may process the ASR output data710, while recognizers863not indicated in the shortlister component850's output may not process the ASR output data710. The “shortlisted” recognizers863may process the ASR output data710in parallel, in series, partially in parallel, etc. For example, if ASR output data710potentially relates to both a communications domain and a music domain, a recognizer associated with the communications domain may process the ASR output data710in parallel, or partially in parallel, with a recognizer associated with the music domain processing the ASR output data710.

Each recognizer863may include a named entity recognition (NER) component862. The NER component862attempts to identify grammars and lexical information that may be used to construe meaning with respect to text data input therein. The NER component862identifies portions of text data that correspond to a named entity associated with a domain, associated with the recognizer863implementing the NER component862. The NER component862(or other component of the NLU component560) may also determine whether a word refers to an entity whose identity is not explicitly mentioned in the text data, for example “him,” “her,” “it” or other anaphora, exophora, or the like.

Each recognizer863, and more specifically each NER component862, may be associated with a particular grammar database876, a particular set of intents/actions874, and a particular personalized lexicon886. The grammar databases876, and intents/actions874may be stored in an NLU storage873. Each gazetteer884may include domain/skill-indexed lexical information associated with a particular user and/or device110. For example, a Gazetteer A (884a) includes skill-indexed lexical information886aato886an. A user's music domain lexical information might include album titles, artist names, and song names, for example, whereas a user's communications domain lexical information might include the names of contacts. Since every user's music collection and contact list is presumably different. This personalized information improves later performed entity resolution.

An NER component862applies grammar information876and lexical information886associated with a domain (associated with the recognizer863implementing the NER component862) to determine a mention of one or more entities in text data. In this manner, the NER component862identifies “slots” (each corresponding to one or more particular words in text data) that may be useful for later processing. The NER component862may also label each slot with a type (e.g., noun, place, city, artist name, song name, etc.).

Each grammar database876includes the names of entities (i.e., nouns) commonly found in speech about the particular domain to which the grammar database876relates, whereas the lexical information886is personalized to the user and/or the device110from which the user input originated. For example, a grammar database876associated with a shopping domain may include a database of words commonly used when people discuss shopping.

A downstream process called entity resolution (discussed in detail elsewhere herein) links a slot of text data to a specific entity known to the system. To perform entity resolution, the NLU component560may utilize gazetteer information (884a-884n) stored in an entity library storage882. The gazetteer information884may be used to match text data (representing a portion of the user input) with text data representing known entities, such as song titles, contact names, etc. Gazetteers884may be linked to users (e.g., a particular gazetteer may be associated with a specific user's music collection), may be linked to certain domains (e.g., a shopping domain, a music domain, a video domain, etc.), or may be organized in a variety of other ways.

Each recognizer863may also include an intent classification (IC) component864. An IC component864parses text data to determine an intent(s) (associated with the domain associated with the recognizer863implementing the IC component864) that potentially represents the user input. An intent represents to an action a user desires be performed. An IC component864may communicate with a database874of words linked to intents. For example, a music intent database may link words and phrases such as “quiet,” “volume off,” and “mute” to a <Mute> intent. An IC component864identifies potential intents by comparing words and phrases in text data (representing at least a portion of the user input) to the words and phrases in an intents database874(associated with the domain that is associated with the recognizer863implementing the IC component864).

The intents identifiable by a specific IC component864are linked to domain-specific (i.e., the domain associated with the recognizer863implementing the IC component864) grammar frameworks876with “slots” to be filled. Each slot of a grammar framework876corresponds to a portion of text data that the system believes corresponds to an entity. For example, a grammar framework876corresponding to a <PlayMusic> intent may correspond to text data sentence structures such as “Play {Artist Name},” “Play {Album Name},” “Play {Song name},” “Play {Song name} by {Artist Name},” etc. However, to make entity resolution more flexible, grammar frameworks876may not be structured as sentences, but rather based on associating slots with grammatical tags.

For example, an NER component862may parse text data to identify words as subject, object, verb, preposition, etc. based on grammar rules and/or models prior to recognizing named entities in the text data. An IC component864(implemented by the same recognizer863as the NER component862) may use the identified verb to identify an intent. The NER component862may then determine a grammar model876associated with the identified intent. For example, a grammar model876for an intent corresponding to <PlayMusic> may specify a list of slots applicable to play the identified “object” and any object modifier (e.g., a prepositional phrase), such as {Artist Name}, {Album Name}, {Song name}, etc. The NER component862may then search corresponding fields in a lexicon886(associated with the domain associated with the recognizer863implementing the NER component862), attempting to match words and phrases in text data the NER component862previously tagged as a grammatical object or object modifier with those identified in the lexicon886.

An NER component862may perform semantic tagging, which is the labeling of a word or combination of words according to their type/semantic meaning. An NER component862may parse text data using heuristic grammar rules, or a model may be constructed using techniques such as Hidden Markov Models, maximum entropy models, log linear models, conditional random fields (CRF), and the like. For example, an NER component862implemented by a music domain recognizer may parse and tag text data corresponding to “play mother's little helper by the rolling stones” as {Verb}: “Play,” {Object}: “mother's little helper,” {Object Preposition}: “by,” and {Object Modifier}: “the rolling stones.” The NER component862identifies “Play” as a verb based on a word database associated with the music domain, which an IC component864(also implemented by the music domain recognizer) may determine corresponds to a <PlayMusic> intent. At this stage, no determination has been made as to the meaning of “mother's little helper” or “the rolling stones,” but based on grammar rules and models, the NER component862has determined the text of these phrases relates to the grammatical object (i.e., entity) of the user input represented in the text data.

The shortlister component850may receive ASR output data710output from the ASR component550or output from the device110b(as illustrated inFIG.9). The ASR component550may embed the ASR output data710into a form processable by a trained model(s) using sentence embedding techniques as known in the art. Sentence embedding results in the ASR output data710including text in a structure that enables the trained models of the shortlister component850to operate on the ASR output data710. For example, an embedding of the ASR output data710may be a vector representation of the ASR output data710.

The shortlister component850may make binary determinations (e.g., yes or no) regarding which domains relate to the ASR output data710. The shortlister component850may make such determinations using the one or more trained models described herein above. If the shortlister component850implements a single trained model for each domain, the shortlister component850may simply run the models that are associated with enabled domains as indicated in a user profile associated with the device110and/or user that originated the user input.

The shortlister component850may generate n-best list data915representing domains that may execute with respect to the user input represented in the ASR output data710. The size of the n-best list represented in the n-best list data915is configurable. In an example, the n-best list data915may indicate every domain of the system as well as contain an indication, for each domain, regarding whether the domain is likely capable to execute the user input represented in the ASR output data710. In another example, instead of indicating every domain of the system, the n-best list data915may only indicate the domains that are likely to be able to execute the user input represented in the ASR output data710. In yet another example, the shortlister component850may implement thresholding such that the n-best list data915may indicate no more than a maximum number of domains that may execute the user input represented in the ASR output data710. In an example, the threshold number of domains that may be represented in the n-best list data915is ten. In another example, the domains included in the n-best list data915may be limited by a threshold a score, where only domains indicating a likelihood to handle the user input is above a certain score (as determined by processing the ASR output data710by the shortlister component850relative to such domains) are included in the n-best list data915.

The ASR output data710may correspond to more than one ASR hypothesis. When this occurs, the shortlister component850may output a different n-best list (represented in the n-best list data915) for each ASR hypothesis. Alternatively, the shortlister component850may output a single n-best list representing the domains that are related to the multiple ASR hypotheses represented in the ASR output data710.

As indicated above, the shortlister component850may implement thresholding such that an n-best list output therefrom may include no more than a threshold number of entries. If the ASR output data710includes more than one ASR hypothesis, the n-best list output by the shortlister component850may include no more than a threshold number of entries irrespective of the number of ASR hypotheses output by the ASR component550. Alternatively or in addition, the n-best list output by the shortlister component850may include no more than a threshold number of entries for each ASR hypothesis (e.g., no more than five entries for a first ASR hypothesis, no more than five entries for a second ASR hypothesis, etc.).

In addition to making a binary determination regarding whether a domain potentially relates to the ASR output data710, the shortlister component850may generate confidence scores representing likelihoods that domains relate to the ASR output data710. If the shortlister component850implements a different trained model for each domain, the shortlister component850may generate a different confidence score for each individual domain trained model that is run. If the shortlister component850runs the models of every domain when ASR output data710is received, the shortlister component850may generate a different confidence score for each domain of the system. If the shortlister component850runs the models of only the domains that are associated with skills indicated as enabled in a user profile associated with the device110and/or user that originated the user input, the shortlister component850may only generate a different confidence score for each domain associated with at least one enabled skill. If the shortlister component850implements a single trained model with domain specifically trained portions, the shortlister component850may generate a different confidence score for each domain who's specifically trained portion is run. The shortlister component850may perform matrix vector modification to obtain confidence scores for all domains of the system in a single instance of processing of the ASR output data710.

N-best list data915including confidence scores that may be output by the shortlister component850may be represented as, for example:Search domain, 0.67Recipe domain, 0.62Information domain, 0.57Shopping domain, 0.42
As indicated, the confidence scores output by the shortlister component850may be numeric values. The confidence scores output by the shortlister component850may alternatively be binned values (e.g., high, medium, low).

The n-best list may only include entries for domains having a confidence score satisfying (e.g., equaling or exceeding) a minimum threshold confidence score. Alternatively, the shortlister component850may include entries for all domains associated with user enabled skills, even if one or more of the domains are associated with confidence scores that do not satisfy the minimum threshold confidence score.

The shortlister component850may consider other data920when determining which domains may relate to the user input represented in the ASR output data710as well as respective confidence scores. The other data920may include usage history data associated with the device110and/or user that originated the user input. For example, a confidence score of a domain may be increased if user inputs originated by the device110and/or user routinely invoke the domain. Conversely, a confidence score of a domain may be decreased if user inputs originated by the device110and/or user rarely invoke the domain. Thus, the other data920may include an indicator of the user associated with the ASR output data710.

The other data920may be character embedded prior to being input to the shortlister component850. The other data920may alternatively be embedded using other techniques known in the art prior to being input to the shortlister component850.

The other data920may also include data indicating the domains associated with skills that are enabled with respect to the device110and/or user that originated the user input. The shortlister component850may use such data to determine which domain-specific trained models to run. That is, the shortlister component850may determine to only run the trained models associated with domains that are associated with user-enabled skills. The shortlister component850may alternatively use such data to alter confidence scores of domains.

As an example, considering two domains, a first domain associated with at least one enabled skill and a second domain not associated with any user-enabled skills of the user that originated the user input, the shortlister component850may run a first model specific to the first domain as well as a second model specific to the second domain. Alternatively, the shortlister component850may run a model configured to determine a score for each of the first and second domains. The shortlister component850may determine a same confidence score for each of the first and second domains in the first instance. The shortlister component850may then alter those confidence scores based on which domains is associated with at least one skill enabled by the present user. For example, the shortlister component850may increase the confidence score associated with the domain associated with at least one enabled skill while leaving the confidence score associated with the other domain the same. Alternatively, the shortlister component850may leave the confidence score associated with the domain associated with at least one enabled skill the same while decreasing the confidence score associated with the other domain. Moreover, the shortlister component850may increase the confidence score associated with the domain associated with at least one enabled skill as well as decrease the confidence score associated with the other domain.

As indicated, a user profile may indicate which skills a corresponding user has enabled (e.g., authorized to execute using data associated with the user). Such indications may be stored in the profile storage570. When the shortlister component850receives the ASR output data710, the shortlister component850may determine whether profile data associated with the user and/or device110that originated the command includes an indication of enabled skills.

The other data920may also include data indicating the type of the device110. The type of a device may indicate the output capabilities of the device. For example, a type of device may correspond to a device with a visual display, a headless (e.g., displayless) device, whether a device is mobile or stationary, whether a device includes audio playback capabilities, whether a device includes a camera, other device hardware configurations, etc. The shortlister component850may use such data to determine which domain-specific trained models to run. For example, if the device110corresponds to a displayless type device, the shortlister component850may determine not to run trained models specific to domains that output video data. The shortlister component850may alternatively use such data to alter confidence scores of domains.

As an example, considering two domains, one that outputs audio data and another that outputs video data, the shortlister component850may run a first model specific to the domain that generates audio data as well as a second model specific to the domain that generates video data. Alternatively the shortlister component850may run a model configured to determine a score for each domain. The shortlister component850may determine a same confidence score for each of the domains in the first instance. The shortlister component850may then alter the original confidence scores based on the type of the device110that originated the user input corresponding to the ASR output data710. For example, if the device110is a displayless device, the shortlister component850may increase the confidence score associated with the domain that generates audio data while leaving the confidence score associated with the domain that generates video data the same. Alternatively, if the device110is a displayless device, the shortlister component850may leave the confidence score associated with the domain that generates audio data the same while decreasing the confidence score associated with the domain that generates video data. Moreover, if the device110is a displayless device, the shortlister component850may increase the confidence score associated with the domain that generates audio data as well as decrease the confidence score associated with the domain that generates video data.

The type of device information represented in the other data920may represent output capabilities of the device to be used to output content to the user, which may not necessarily be the user input originating device. For example, a user may input a spoken user input corresponding to “play Game of Thrones” to a device not including a display. The system may determine a smart TV or other display device (associated with the same user profile) for outputting Game of Thrones. Thus, the other data920may represent the smart TV of other display device, and not the displayless device that captured the spoken user input.

The other data920may also include data indicating the user input originating device's speed, location, or other mobility information. For example, the device may correspond to a vehicle including a display. If the vehicle is moving, the shortlister component850may decrease the confidence score associated with a domain that generates video data as it may be undesirable to output video content to a user while the user is driving. The device may output data to the system(s)120indicating when the device is moving.

The other data920may also include data indicating a currently invoked domain. For example, a user may speak a first (e.g., a previous) user input causing the system to invoke a music domain skill to output music to the user. As the system is outputting music to the user, the system may receive a second (e.g., the current) user input. The shortlister component850may use such data to alter confidence scores of domains. For example, the shortlister component850may run a first model specific to a first domain as well as a second model specific to a second domain. Alternatively, the shortlister component850may run a model configured to determine a score for each domain. The shortlister component850may also determine a same confidence score for each of the domains in the first instance. The shortlister component850may then alter the original confidence scores based on the first domain being invoked to cause the system to output content while the current user input was received. Based on the first domain being invoked, the shortlister component850may (i) increase the confidence score associated with the first domain while leaving the confidence score associated with the second domain the same, (ii) leave the confidence score associated with the first domain the same while decreasing the confidence score associated with the second domain, or (iii) increase the confidence score associated with the first domain as well as decrease the confidence score associated with the second domain.

The thresholding implemented with respect to the n-best list data915generated by the shortlister component850as well as the different types of other data920considered by the shortlister component850are configurable. For example, the shortlister component850may update confidence scores as more other data920is considered. For further example, the n-best list data915may exclude relevant domains if thresholding is implemented. Thus, for example, the shortlister component850may include an indication of a domain in the n-best list915unless the shortlister component850is one hundred percent confident that the domain may not execute the user input represented in the ASR output data710(e.g., the shortlister component850determines a confidence score of zero for the domain).

The shortlister component850may send the ASR output data710to recognizers863associated with domains represented in the n-best list data915. Alternatively, the shortlister component850may send the n-best list data915or some other indicator of the selected subset of domains to another component (such as the orchestrator component530) which may in turn send the ASR output data710to the recognizers863corresponding to the domains included in the n-best list data915or otherwise indicated in the indicator. If the shortlister component850generates an n-best list representing domains without any associated confidence scores, the shortlister component850/orchestrator component530may send the ASR output data710to recognizers863associated with domains that the shortlister component850determines may execute the user input. If the shortlister component850generates an n-best list representing domains with associated confidence scores, the shortlister component850/orchestrator component530may send the ASR output data710to recognizers863associated with domains associated with confidence scores satisfying (e.g., meeting or exceeding) a threshold minimum confidence score.

A recognizer863may output tagged text data generated by an NER component862and an IC component864, as described herein above. The NLU component560may compile the output tagged text data of the recognizers863into a single cross-domain n-best list940and may send the cross-domain n-best list940to a pruning component950. Each entry of tagged text (e.g., each NLU hypothesis) represented in the cross-domain n-best list data940may be associated with a respective score indicating a likelihood that the NLU hypothesis corresponds to the domain associated with the recognizer863from which the NLU hypothesis was output. For example, the cross-domain n-best list data940may be represented as (with each line corresponding to a different NLU hypothesis):[0.95] Intent: <PlayMusic> ArtistName: Beethoven SongName: Waldstein Sonata[0.70] Intent: <Play Video> ArtistName: Beethoven VideoName: Waldstein Sonata[0.01] Intent: <PlayMusic> ArtistName: Beethoven AlbumName: Waldstein Sonata[0.01] Intent: <PlayMusic> SongName: Waldstein Sonata

The pruning component950may sort the NLU hypotheses represented in the cross-domain n-best list data940according to their respective scores. The pruning component950may perform score thresholding with respect to the cross-domain NLU hypotheses. For example, the pruning component950may select NLU hypotheses associated with scores satisfying (e.g., meeting and/or exceeding) a threshold score. The pruning component950may also or alternatively perform number of NLU hypothesis thresholding. For example, the pruning component950may select the top scoring NLU hypothesis(es). The pruning component950may output a portion of the NLU hypotheses input thereto. The purpose of the pruning component950is to create a reduced list of NLU hypotheses so that downstream, more resource intensive, processes may only operate on the NLU hypotheses that most likely represent the user's intent.

The NLU component560may include a light slot filler component952. The light slot filler component952can take text from slots represented in the NLU hypotheses output by the pruning component950and alter them to make the text more easily processed by downstream components. The light slot filler component952may perform low latency operations that do not involve heavy operations such as reference to a knowledge base (e.g.,872. The purpose of the light slot filler component952is to replace words with other words or values that may be more easily understood by downstream components. For example, if a NLU hypothesis includes the word “tomorrow,” the light slot filler component952may replace the word “tomorrow” with an actual date for purposes of downstream processing. Similarly, the light slot filler component952may replace the word “CD” with “album” or the words “compact disc.” The replaced words are then included in the cross-domain n-best list data960.

The cross-domain n-best list data960may be input to an entity resolution component970. The entity resolution component970can apply rules or other instructions to standardize labels or tokens from previous stages into an intent/slot representation. The precise transformation may depend on the domain. For example, for a travel domain, the entity resolution component970may transform text corresponding to “Boston airport” to the standard BOS three-letter code referring to the airport. The entity resolution component970can refer to a knowledge base (e.g.,872) that is used to specifically identify the precise entity referred to in each slot of each NLU hypothesis represented in the cross-domain n-best list data960. Specific intent/slot combinations may also be tied to a particular source, which may then be used to resolve the text. In the example “play songs by the stones,” the entity resolution component970may reference a personal music catalog, Amazon Music account, a user profile, or the like. The entity resolution component970may output an altered n-best list that is based on the cross-domain n-best list960but that includes more detailed information (e.g., entity IDs) about the specific entities mentioned in the slots and/or more detailed slot data that can eventually be used by a skill. The NLU component560may include multiple entity resolution components970and each entity resolution component970may be specific to one or more domains.

The NLU component560may include a reranker990. The reranker990may assign a particular confidence score to each NLU hypothesis input therein. The confidence score of a particular NLU hypothesis may be affected by whether the NLU hypothesis has unfilled slots. For example, if a NLU hypothesis includes slots that are all filled/resolved, that NLU hypothesis may be assigned a higher confidence score than another NLU hypothesis including at least some slots that are unfilled/unresolved by the entity resolution component970.

The reranker990may apply re-scoring, biasing, or other techniques. The reranker990may consider not only the data output by the entity resolution component970, but may also consider other data991. The other data991may include a variety of information. For example, the other data991may include skill rating or popularity data. For example, if one skill has a high rating, the reranker990may increase the score of a NLU hypothesis that may be processed by the skill. The other data991may also include information about skills that have been enabled by the user that originated the user input. For example, the reranker990may assign higher scores to NLU hypothesis that may be processed by enabled skills than NLU hypothesis that may be processed by non-enabled skills. The other data991may also include data indicating user usage history, such as if the user that originated the user input regularly uses a particular skill or does so at particular times of day. The other data991may additionally include data indicating date, time, location, weather, type of device110, user identifier, context, as well as other information. For example, the reranker990may consider when any particular skill is currently active (e.g., music being played, a game being played, etc.).

As illustrated and described, the entity resolution component970is implemented prior to the reranker990. The entity resolution component970may alternatively be implemented after the reranker990. Implementing the entity resolution component970after the reranker990limits the NLU hypotheses processed by the entity resolution component970to only those hypotheses that successfully pass through the reranker990.

The reranker990may be a global reranker (e.g., one that is not specific to any particular domain). Alternatively, the NLU component560may implement one or more domain-specific rerankers. Each domain-specific reranker may rerank NLU hypotheses associated with the domain. Each domain-specific reranker may output an n-best list of reranked hypotheses (e.g., 5-10 hypotheses).

The NLU component560may perform NLU processing described above with respect to domains associated with skills wholly implemented as part of the system(s)120(e.g., designated590inFIG.5). The NLU component560may separately perform NLU processing described above with respect to domains associated with skills that are at least partially implemented as part of the skill system(s)525. In an example, the shortlister component850may only process with respect to these latter domains. Results of these two NLU processing paths may be merged into NLU output data985, which may be sent to a post-NLU ranker565, which may be implemented by the system(s)120.

The post-NLU ranker565may include a statistical component that produces a ranked list of intent/skill pairs with associated confidence scores. Each confidence score may indicate an adequacy of the skill's execution of the intent with respect to NLU results data associated with the skill. The post-NLU ranker565may operate one or more trained models configured to process the NLU results data985, skill result data930, and the other data920in order to output ranked output data925. The ranked output data925may include an n-best list where the NLU hypotheses in the NLU results data985are reordered such that the n-best list in the ranked output data925represents a prioritized list of skills to respond to a user input as determined by the post-NLU ranker565. The ranked output data925may also include (either as part of an n-best list or otherwise) individual respective scores corresponding to skills where each score indicates a probability that the skill (and/or its respective result data) corresponds to the user input.

The system may be configured with thousands, tens of thousands, etc. skills. The post-NLU ranker565enables the system to better determine the best skill to execute the user input. For example, first and second NLU hypotheses in the NLU results data985may substantially correspond to each other (e.g., their scores may be significantly similar), even though the first NLU hypothesis may be processed by a first skill and the second NLU hypothesis may be processed by a second skill. The first NLU hypothesis may be associated with a first confidence score indicating the system's confidence with respect to NLU processing performed to generate the first NLU hypothesis. Moreover, the second NLU hypothesis may be associated with a second confidence score indicating the system's confidence with respect to NLU processing performed to generate the second NLU hypothesis. The first confidence score may be similar or identical to the second confidence score. The first confidence score and/or the second confidence score may be a numeric value (e.g., from 0.0 to 1.0). Alternatively, the first confidence score and/or the second confidence score may be a binned value (e.g., low, medium, high).

The post-NLU ranker565(or other scheduling component such as orchestrator component530) may solicit the first skill and the second skill to provide potential result data930based on the first NLU hypothesis and the second NLU hypothesis, respectively. For example, the post-NLU ranker565may send the first NLU hypothesis to the first skill590aalong with a request for the first skill590ato at least partially execute with respect to the first NLU hypothesis. The post-NLU ranker565may also send the second NLU hypothesis to the second skill590balong with a request for the second skill590bto at least partially execute with respect to the second NLU hypothesis. The post-NLU ranker565receives, from the first skill590a, first result data930agenerated from the first skill590a's execution with respect to the first NLU hypothesis. The post-NLU ranker565also receives, from the second skill590b, second results data930bgenerated from the second skill590b's execution with respect to the second NLU hypothesis.

The result data930may include various portions. For example, the result data930may include content (e.g., audio data, text data, and/or video data) to be output to a user. The result data930may also include a unique identifier used by the system(s)120and/or the skill system(s)525to locate the data to be output to a user. The result data930may also include an instruction. For example, if the user input corresponds to “turn on the light,” the result data930may include an instruction causing the system to turn on a light associated with a profile of the device (110a/110b) and/or user.

The post-NLU ranker565may consider the first result data930aand the second result data930bto alter the first confidence score and the second confidence score of the first NLU hypothesis and the second NLU hypothesis, respectively. That is, the post-NLU ranker565may generate a third confidence score based on the first result data930aand the first confidence score. The third confidence score may correspond to how likely the post-NLU ranker565determines the first skill will correctly respond to the user input. The post-NLU ranker565may also generate a fourth confidence score based on the second result data930band the second confidence score. One skilled in the art will appreciate that a first difference between the third confidence score and the fourth confidence score may be greater than a second difference between the first confidence score and the second confidence score. The post-NLU ranker565may also consider the other data920to generate the third confidence score and the fourth confidence score. While it has been described that the post-NLU ranker565may alter the confidence scores associated with first and second NLU hypotheses, one skilled in the art will appreciate that the post-NLU ranker565may alter the confidence scores of more than two NLU hypotheses. The post-NLU ranker565may select the result data930associated with the skill590with the highest altered confidence score to be the data output in response to the current user input. The post-NLU ranker565may also consider the ASR output data710to alter the NLU hypotheses confidence scores.

The orchestrator component530may, prior to sending the NLU results data985to the post-NLU ranker565, associate intents in the NLU hypotheses with skills590. For example, if a NLU hypothesis includes a <PlayMusic> intent, the orchestrator component530may associate the NLU hypothesis with one or more skills590that can execute the <PlayMusic> intent. Thus, the orchestrator component530may send the NLU results data985, including NLU hypotheses paired with skills590, to the post-NLU ranker565. In response to ASR output data710corresponding to “what should I do for dinner today,” the orchestrator component530may generates pairs of skills590with associated NLU hypotheses corresponding to:Skill 1/NLU hypothesis including <Help> intentSkill 2/NLU hypothesis including <Order> intentSkill 3/NLU hypothesis including <DishType> intent

The post-NLU ranker565queries each skill590, paired with a NLU hypothesis in the NLU output data985, to provide result data930based on the NLU hypothesis with which it is associated. That is, with respect to each skill, the post-NLU ranker565colloquially asks the each skill “if given this NLU hypothesis, what would you do with it.” According to the above example, the post-NLU ranker565may send skills590the following data:Skill 1: First NLU hypothesis including <Help> intent indicatorSkill 2: Second NLU hypothesis including <Order> intent indicatorSkill 3: Third NLU hypothesis including <DishType> intent indicator
The post-NLU ranker565may query each of the skills590in parallel or substantially in parallel.

A skill590may provide the post-NLU ranker565with various data and indications in response to the post-NLU ranker565soliciting the skill590for result data930. A skill590may simply provide the post-NLU ranker565with an indication of whether or not the skill can execute with respect to the NLU hypothesis it received. A skill590may also or alternatively provide the post-NLU ranker565with output data generated based on the NLU hypothesis it received. In some situations, a skill590may need further information in addition to what is represented in the received NLU hypothesis to provide output data responsive to the user input. In these situations, the skill590may provide the post-NLU ranker565with result data930indicating slots of a framework that the skill590further needs filled or entities that the skill590further needs resolved prior to the skill590being able to provided result data930responsive to the user input. The skill590may also provide the post-NLU ranker565with an instruction and/or computer-generated speech indicating how the skill590recommends the system solicit further information needed by the skill590. The skill590may further provide the post-NLU ranker565with an indication of whether the skill590will have all needed information after the user provides additional information a single time, or whether the skill590will need the user to provide various kinds of additional information prior to the skill590having all needed information. According to the above example, skills590may provide the post-NLU ranker565with the following:Skill 1: indication representing the skill can execute with respect to a NLU hypothesis including the <Help> intent indicatorSkill 2: indication representing the skill needs to the system to obtain further informationSkill 3: indication representing the skill can provide numerous results in response to the third NLU hypothesis including the <DishType> intent indicator

Result data930includes an indication provided by a skill590indicating whether or not the skill590can execute with respect to a NLU hypothesis; data generated by a skill590based on a NLU hypothesis; as well as an indication provided by a skill590indicating the skill590needs further information in addition to what is represented in the received NLU hypothesis.

The post-NLU ranker565uses the result data930provided by the skills590to alter the NLU processing confidence scores generated by the reranker990. That is, the post-NLU ranker565uses the result data930provided by the queried skills590to create larger differences between the NLU processing confidence scores generated by the reranker990. Without the post-NLU ranker565, the system may not be confident enough to determine an output in response to a user input, for example when the NLU hypotheses associated with multiple skills are too close for the system to confidently determine a single skill590to invoke to respond to the user input. For example, if the system does not implement the post-NLU ranker565, the system may not be able to determine whether to obtain output data from a general reference information skill or a medical information skill in response to a user input corresponding to “what is acne.”

The post-NLU ranker565may prefer skills590that provide result data930responsive to NLU hypotheses over skills590that provide result data930corresponding to an indication that further information is needed, as well as skills590that provide result data930indicating they can provide multiple responses to received NLU hypotheses. For example, the post-NLU ranker565may generate a first score for a first skill590athat is greater than the first skill's NLU confidence score based on the first skill590aproviding result data930aincluding a response to a NLU hypothesis. For further example, the post-NLU ranker565may generate a second score for a second skill590bthat is less than the second skill's NLU confidence score based on the second skill590bproviding result data930bindicating further information is needed for the second skill590bto provide a response to a NLU hypothesis. Yet further, for example, the post-NLU ranker565may generate a third score for a third skill590cthat is less than the third skill's NLU confidence score based on the third skill590cproviding result data930cindicating the third skill590ccan provide multiple responses to a NLU hypothesis.

The post-NLU ranker565may consider other data920in determining scores. The other data920may include rankings associated with the queried skills590. A ranking may be a system ranking or a user-specific ranking. A ranking may indicate a veracity of a skill from the perspective of one or more users of the system. For example, the post-NLU ranker565may generate a first score for a first skill590athat is greater than the first skill's NLU processing confidence score based on the first skill590abeing associated with a high ranking. For further example, the post-NLU ranker565may generate a second score for a second skill590bthat is less than the second skill's NLU processing confidence score based on the second skill590bbeing associated with a low ranking.

The other data920may include information indicating whether or not the user that originated the user input has enabled one or more of the queried skills590. For example, the post-NLU ranker565may generate a first score for a first skill590athat is greater than the first skill's NLU processing confidence score based on the first skill590abeing enabled by the user that originated the user input. For further example, the post-NLU ranker565may generate a second score for a second skill590bthat is less than the second skill's NLU processing confidence score based on the second skill590bnot being enabled by the user that originated the user input. When the post-NLU ranker565receives the NLU results data985, the post-NLU ranker565may determine whether profile data, associated with the user and/or device that originated the user input, includes indications of enabled skills.

The other data920may include information indicating output capabilities of a device that will be used to output content, responsive to the user input, to the user. The system may include devices that include speakers but not displays, devices that include displays but not speakers, and devices that include speakers and displays. If the device that will output content responsive to the user input includes one or more speakers but not a display, the post-NLU ranker565may increase the NLU processing confidence score associated with a first skill configured to output audio data and/or decrease the NLU processing confidence score associated with a second skill configured to output visual data (e.g., image data and/or video data). If the device that will output content responsive to the user input includes a display but not one or more speakers, the post-NLU ranker565may increase the NLU processing confidence score associated with a first skill configured to output visual data and/or decrease the NLU processing confidence score associated with a second skill configured to output audio data.

The other data920may include information indicating the veracity of the result data930provided by a skill590. For example, if a user says “tell me a recipe for pasta sauce,” a first skill590amay provide the post-NLU ranker565with first result data930acorresponding to a first recipe associated with a five star rating and a second skill590bmay provide the post-NLU ranker565with second result data930bcorresponding to a second recipe associated with a one star rating. In this situation, the post-NLU ranker565may increase the NLU processing confidence score associated with the first skill590abased on the first skill590aproviding the first result data930aassociated with the five star rating and/or decrease the NLU processing confidence score associated with the second skill590bbased on the second skill590bproviding the second result data930bassociated with the one star rating.

The other data920may include information indicating the type of device that originated the user input. For example, the device may correspond to a “hotel room” type if the device is located in a hotel room. If a user inputs a command corresponding to “order me food” to the device located in the hotel room, the post-NLU ranker565may increase the NLU processing confidence score associated with a first skill590acorresponding to a room service skill associated with the hotel and/or decrease the NLU processing confidence score associated with a second skill590bcorresponding to a food skill not associated with the hotel.

The other data920may include information indicating a location of the device and/or user that originated the user input. The system may be configured with skills590that may only operate with respect to certain geographic locations. For example, a user may provide a user input corresponding to “when is the next train to Portland.” A first skill590amay operate with respect to trains that arrive at, depart from, and pass through Portland, Oregon. A second skill590bmay operate with respect to trains that arrive at, depart from, and pass through Portland, Maine. If the device and/or user that originated the user input is located in Seattle, Washington, the post-NLU ranker565may increase the NLU processing confidence score associated with the first skill590aand/or decrease the NLU processing confidence score associated with the second skill590b. Likewise, if the device and/or user that originated the user input is located in Boston, Massachusetts, the post-NLU ranker565may increase the NLU processing confidence score associated with the second skill590band/or decrease the NLU processing confidence score associated with the first skill590a.

The other data920may include information indicating a time of day. The system may be configured with skills590that operate with respect to certain times of day. For example, a user may provide a user input corresponding to “order me food.” A first skill590amay generate first result data930acorresponding to breakfast. A second skill590bmay generate second result data930bcorresponding to dinner. If the system(s)120receives the user input in the morning, the post-NLU ranker565may increase the NLU processing confidence score associated with the first skill590aand/or decrease the NLU processing score associated with the second skill590b. If the system(s)120receives the user input in the afternoon or evening, the post-NLU ranker565may increase the NLU processing confidence score associated with the second skill590band/or decrease the NLU processing confidence score associated with the first skill590a.

The other data920may include information indicating user preferences. The system may include multiple skills590configured to execute in substantially the same manner. For example, a first skill590aand a second skill590bmay both be configured to order food from respective restaurants. The system may store a user preference (e.g., in the profile storage570) that is associated with the user that provided the user input to the system(s)120as well as indicates the user prefers the first skill590aover the second skill590b. Thus, when the user provides a user input that may be executed by both the first skill590aand the second skill590b, the post-NLU ranker565may increase the NLU processing confidence score associated with the first skill590aand/or decrease the NLU processing confidence score associated with the second skill590b.

The other data920may include information indicating system usage history associated with the user that originated the user input. For example, the system usage history may indicate the user originates user inputs that invoke a first skill590amore often than the user originates user inputs that invoke a second skill590b. Based on this, if the present user input may be executed by both the first skill590aand the second skill590b, the post-NLU ranker565may increase the NLU processing confidence score associated with the first skill590aand/or decrease the NLU processing confidence score associated with the second skill590b.

The other data920may include information indicating a speed at which the device110that originated the user input is traveling. For example, the device110may be located in a moving vehicle, or may be a moving vehicle. When a device110is in motion, the system may prefer audio outputs rather than visual outputs to decrease the likelihood of distracting the user (e.g., a driver of a vehicle). Thus, for example, if the device110that originated the user input is moving at or above a threshold speed (e.g., a speed above an average user's walking speed), the post-NLU ranker565may increase the NLU processing confidence score associated with a first skill590athat generates audio data. The post-NLU ranker565may also or alternatively decrease the NLU processing confidence score associated with a second skill590bthat generates image data or video data.

The other data920may include information indicating how long it took a skill590to provide result data930to the post-NLU ranker565. When the post-NLU ranker565multiple skills590for result data930, the skills590may respond to the queries at different speeds. The post-NLU ranker565may implement a latency budget. For example, if the post-NLU ranker565determines a skill590responds to the post-NLU ranker565within a threshold amount of time from receiving a query from the post-NLU ranker565, the post-NLU ranker565may increase the NLU processing confidence score associated with the skill590. Conversely, if the post-NLU ranker565determines a skill590does not respond to the post-NLU ranker565within a threshold amount of time from receiving a query from the post-NLU ranker565, the post-NLU ranker565may decrease the NLU processing confidence score associated with the skill590.

It has been described that the post-NLU ranker565uses the other data920to increase and decrease NLU processing confidence scores associated with various skills590that the post-NLU ranker565has already requested result data from. Alternatively, the post-NLU ranker565may use the other data920to determine which skills590to request result data from. For example, the post-NLU ranker565may use the other data920to increase and/or decrease NLU processing confidence scores associated with skills590associated with the NLU results data985output by the NLU component560. The post-NLU ranker565may select n-number of top scoring altered NLU processing confidence scores. The post-NLU ranker565may then request result data930from only the skills590associated with the selected n-number of NLU processing confidence scores.

As described, the post-NLU ranker565may request result data930from all skills590associated with the NLU results data985output by the NLU component560. Alternatively, the system(s)120may prefer result data930from skills implemented entirely by the system(s)120rather than skills at least partially implemented by the skill system(s)525. Therefore, in the first instance, the post-NLU ranker565may request result data930from only skills associated with the NLU results data985and entirely implemented by the system(s)120. The post-NLU ranker565may only request result data930from skills associated with the NLU results data985, and at least partially implemented by the skill system(s)525, if none of the skills, wholly implemented by the system(s)120, provide the post-NLU ranker565with result data930indicating either data response to the NLU results data985, an indication that the skill can execute the user input, or an indication that further information is needed.

As indicated above, the post-NLU ranker565may request result data930from multiple skills590. If one of the skills590provides result data930indicating a response to a NLU hypothesis and the other skills provide result data930indicating either they cannot execute or they need further information, the post-NLU ranker565may select the result data930including the response to the NLU hypothesis as the data to be output to the user. If more than one of the skills590provides result data930indicating responses to NLU hypotheses, the post-NLU ranker565may consider the other data920to generate altered NLU processing confidence scores, and select the result data930of the skill associated with the greatest score as the data to be output to the user.

A system that does not implement the post-NLU ranker565may select the highest scored NLU hypothesis in the NLU results data985. The system may send the NLU hypothesis to a skill590associated therewith along with a request for output data. In some situations, the skill590may not be able to provide the system with output data. This results in the system indicating to the user that the user input could not be processed even though another skill associated with lower ranked NLU hypothesis could have provided output data responsive to the user input.

The post-NLU ranker565reduces instances of the aforementioned situation. As described, the post-NLU ranker565queries multiple skills associated with the NLU results data985to provide result data930to the post-NLU ranker565prior to the post-NLU ranker565ultimately determining the skill590to be invoked to respond to the user input. Some of the skills590may provide result data930indicating responses to NLU hypotheses while other skills590may providing result data930indicating the skills cannot provide responsive data. Whereas a system not implementing the post-NLU ranker565may select one of the skills590that could not provide a response, the post-NLU ranker565only selects a skill590that provides the post-NLU ranker565with result data corresponding to a response, indicating further information is needed, or indicating multiple responses can be generated.

The post-NLU ranker565may select result data930, associated with the skill590associated with the highest score, for output to the user. Alternatively, the post-NLU ranker565may output ranked output data925indicating skills590and their respective post-NLU ranker rankings. Since the post-NLU ranker565receives result data930, potentially corresponding to a response to the user input, from the skills590prior to post-NLU ranker565selecting one of the skills or outputting the ranked output data925, little to no latency occurs from the time skills provide result data930and the time the system outputs responds to the user.

If the post-NLU ranker565selects result audio data to be output to a user and the system determines content should be output audibly, the post-NLU ranker565(or another component of the system(s)120) may cause the device110aand/or the device110bto output audio corresponding to the result audio data. If the post-NLU ranker565selects result text data to output to a user and the system determines content should be output visually, the post-NLU ranker565(or another component of the system(s)120) may cause the device110bto display text corresponding to the result text data. If the post-NLU ranker565selects result audio data to output to a user and the system determines content should be output visually, the post-NLU ranker565(or another component of the system(s)120) may send the result audio data to the ASR component550. The ASR component550may generate output text data corresponding to the result audio data. The system(s)120may then cause the device110bto display text corresponding to the output text data. If the post-NLU ranker565selects result text data to output to a user and the system determines content should be output audibly, the post-NLU ranker565(or another component of the system(s)120) may send the result text data to the TTS component580. The TTS component580may generate output audio data (corresponding to computer-generated speech) based on the result text data. The system(s)120may then cause the device110aand/or the device110bto output audio corresponding to the output audio data.

As described, a skill590may provide result data930either indicating a response to the user input, indicating more information is needed for the skill590to provide a response to the user input, or indicating the skill590cannot provide a response to the user input. If the skill590associated with the highest post-NLU ranker score provides the post-NLU ranker565with result data930indicating a response to the user input, the post-NLU ranker565(or another component of the system(s)120, such as the orchestrator component530) may simply cause content corresponding to the result data930to be output to the user. For example, the post-NLU ranker565may send the result data930to the orchestrator component530. The orchestrator component530may cause the result data930to be sent to the device (110a/110b), which may output audio and/or display text corresponding to the result data930. The orchestrator component530may send the result data930to the ASR component550to generate output text data and/or may send the result data930to the TTS component580to generate output audio data, depending on the situation.

The skill590associated with the highest post-NLU ranker score may provide the post-NLU ranker565with result data930indicating more information is needed as well as instruction data. The instruction data may indicate how the skill590recommends the system obtain the needed information. For example, the instruction data may correspond to text data or audio data (i.e., computer-generated speech) corresponding to “please indicate——————.” The instruction data may be in a format (e.g., text data or audio data) capable of being output by the device (110a/110b). When this occurs, the post-NLU ranker565may simply cause the received instruction data be output by the device (110a/110b). Alternatively, the instruction data may be in a format that is not capable of being output by the device (110a/110b). When this occurs, the post-NLU ranker565may cause the ASR component550or the TTS component580to process the instruction data, depending on the situation, to generate instruction data that may be output by the device (110a/110b). Once the user provides the system with all further information needed by the skill590, the skill590may provide the system with result data930indicating a response to the user input, which may be output by the system as detailed above.

The system may include “informational” skills590that simply provide the system with information, which the system outputs to the user. The system may also include “transactional” skills590that require a system instruction to execute the user input. Transactional skills590include ride sharing skills, flight booking skills, etc. A transactional skill590may simply provide the post-NLU ranker565with result data930indicating the transactional skill590can execute the user input. The post-NLU ranker565may then cause the system to solicit the user for an indication that the system is permitted to cause the transactional skill590to execute the user input. The user-provided indication may be an audible indication or a tactile indication (e.g., activation of a virtual button or input of text via a virtual keyboard). In response to receiving the user-provided indication, the system may provide the transactional skill590with data corresponding to the indication. In response, the transactional skill590may execute the command (e.g., book a flight, book a train ticket, etc.). Thus, while the system may not further engage an informational skill590after the informational skill590provides the post-NLU ranker565with result data930, the system may further engage a transactional skill590after the transactional skill590provides the post-NLU ranker565with result data930indicating the transactional skill590may execute the user input.

In some instances, the post-NLU ranker565may generate respective scores for first and second skills that are too close (e.g., are not different by at least a threshold difference) for the post-NLU ranker565to make a confident determination regarding which skill should execute the user input. When this occurs, the system may request the user indicate which skill the user prefers to execute the user input. The system may output TTS-generated speech to the user to solicit which skill the user wants to execute the user input.

One or more models implemented by components of the orchestrator component530, post-NLU ranker565, shortlister component850, or other component may be trained and operated according to various machine learning techniques.

Components of a system that may be used to perform unit selection, parametric TTS processing, and/or model-based audio synthesis are shown inFIG.10. As shown inFIG.10, the TTS component/processor580may include a TTS front end1016, a speech synthesis engine1018, TTS unit storage1072, TTS parametric storage1080, and a TTS back end1034. The TTS unit storage1072may include, among other things, voice inventories1078a-1078nthat may include pre-recorded audio segments (called units) to be used by the unit selection engine1030when performing unit selection synthesis as described below. The TTS parametric storage1080may include, among other things, parametric settings1068a-1068nthat may be used by the parametric synthesis engine1032when performing parametric synthesis as described below. A particular set of parametric settings1068may correspond to a particular voice profile (e.g., whispered speech, excited speech, etc.).

In various embodiments of the present disclosure, model-based synthesis of audio data may be performed using by a speech model1022and a TTS front end1016. The TTS front end1016may be the same as front ends used in traditional unit selection or parametric systems. In other embodiments, some or all of the components of the TTS front end1016are based on other trained models. The present disclosure is not, however, limited to any particular type of TTS front end1016. The speech model1022may be used to synthesize speech without requiring the TTS unit storage1072or the TTS parametric storage1080, as described in greater detail below.

TTS component receives text data1010. Although the text data1010inFIG.10is input into the TTS component580, it may be output by other component(s) (such as a skill590, NLU component560, NLG component579or other component) and may be intended for output by the system. Thus in certain instances text data1010may be referred to as “output text data.” Further, the data1010may not necessarily be text, but may include other data (such as symbols, code, other data, etc.) that may reference text (such as an indicator of a word) that is to be synthesized. Thus data1010may come in a variety of forms. The TTS front end1016transforms the data1010(from, for example, an application, user, device, or other data source) into a symbolic linguistic representation, which may include linguistic context features such as phoneme data, punctuation data, syllable-level features, word-level features, and/or emotion, speaker, accent, or other features for processing by the speech synthesis engine1018. The syllable-level features may include syllable emphasis, syllable speech rate, syllable inflection, or other such syllable-level features; the word-level features may include word emphasis, word speech rate, word inflection, or other such word-level features. The emotion features may include data corresponding to an emotion associated with the text data1010, such as surprise, anger, or fear. The speaker features may include data corresponding to a type of speaker, such as sex, age, or profession. The accent features may include data corresponding to an accent associated with the speaker, such as Southern, Boston, English, French, or other such accent.

The TTS front end1016may also process other input data1015, such as text tags or text metadata, that may indicate, for example, how specific words should be pronounced, for example by indicating the desired output speech quality in tags formatted according to the speech synthesis markup language (SSML) or in some other form. For example, a first text tag may be included with text marking the beginning of when text should be whispered (e.g., <begin whisper>) and a second tag may be included with text marking the end of when text should be whispered (e.g., <end whisper>). The tags may be included in the text data1010and/or the text for a TTS request may be accompanied by separate metadata indicating what text should be whispered (or have some other indicated audio characteristic). The speech synthesis engine1018may compare the annotated phonetic units models and information stored in the TTS unit storage1072and/or TTS parametric storage1080for converting the input text into speech. The TTS front end1016and speech synthesis engine1018may include their own controller(s)/processor(s) and memory or they may use the controller/processor and memory of the server120, device110, or other device, for example. Similarly, the instructions for operating the TTS front end1016and speech synthesis engine1018may be located within the TTS component580, within the memory and/or storage of the server120, device110, or within an external device.

Text data1010input into the TTS component580may be sent to the TTS front end1016for processing. The front end1016may include components for performing text normalization, linguistic analysis, linguistic prosody generation, or other such components. During text normalization, the TTS front end1016may first process the text input and generate standard text, converting such things as numbers, abbreviations (such as Apt., St., etc.), symbols ($, %, etc.) into the equivalent of written out words.

During linguistic analysis, the TTS front end1016may analyze the language in the normalized text to generate a sequence of phonetic units corresponding to the input text. This process may be referred to as grapheme-to-phoneme conversion. Phonetic units include symbolic representations of sound units to be eventually combined and output by the system as speech. Various sound units may be used for dividing text for purposes of speech synthesis. The TTS component580may process speech based on phonemes (individual sounds), half-phonemes, di-phones (the last half of one phoneme coupled with the first half of the adjacent phoneme), bi-phones (two consecutive phonemes), syllables, words, phrases, sentences, or other units. Each word may be mapped to one or more phonetic units. Such mapping may be performed using a language dictionary stored by the system, for example in the TTS unit storage1072. The linguistic analysis performed by the TTS front end1016may also identify different grammatical components such as prefixes, suffixes, phrases, punctuation, syntactic boundaries, or the like. Such grammatical components may be used by the TTS component580to craft a natural-sounding audio waveform output. The language dictionary may also include letter-to-sound rules and other tools that may be used to pronounce previously unidentified words or letter combinations that may be encountered by the TTS component580. Generally, the more information included in the language dictionary, the higher quality the speech output.

Based on the linguistic analysis the TTS front end1016may then perform linguistic prosody generation where the phonetic units are annotated with desired prosodic characteristics, also called acoustic features, which indicate how the desired phonetic units are to be pronounced in the eventual output speech. During this stage the TTS front end1016may consider and incorporate any prosodic annotations that accompanied the text input to the TTS component580. Such acoustic features may include syllable-level features, word-level features, emotion, speaker, accent, language, pitch, energy, duration, and the like. Application of acoustic features may be based on prosodic models available to the TTS component580. Such prosodic models indicate how specific phonetic units are to be pronounced in certain circumstances. A prosodic model may consider, for example, a phoneme's position in a syllable, a syllable's position in a word, a word's position in a sentence or phrase, neighboring phonetic units, etc. As with the language dictionary, a prosodic model with more information may result in higher quality speech output than prosodic models with less information. Further, a prosodic model and/or phonetic units may be used to indicate particular speech qualities of the speech to be synthesized, where those speech qualities may match the speech qualities of input speech (for example, the phonetic units may indicate prosodic characteristics to make the ultimately synthesized speech sound like a whisper based on the input speech being whispered).

The output of the TTS front end1016, which may be referred to as a symbolic linguistic representation, may include a sequence of phonetic units annotated with prosodic characteristics. This symbolic linguistic representation may be sent to the speech synthesis engine1018, which may also be known as a synthesizer, for conversion into an audio waveform of speech for output to an audio output device and eventually to a user. The speech synthesis engine1018may be configured to convert the input text into high-quality natural-sounding speech in an efficient manner. Such high-quality speech may be configured to sound as much like a human speaker as possible, or may be configured to be understandable to a listener without attempts to mimic a precise human voice.

The speech synthesis engine1018may perform speech synthesis using one or more different methods. In one method of synthesis called unit selection, described further below, a unit selection engine1030matches the symbolic linguistic representation created by the TTS front end1016against a database of recorded speech, such as a database (e.g., TTS unit storage1072) storing information regarding one or more voice corpuses (e.g., voice inventories1078a-n). Each voice inventory may correspond to various segments of audio that was recorded by a speaking human, such as a voice actor, where the segments are stored in an individual inventory1078as acoustic units (e.g., phonemes, diphones, etc.). Each stored unit of audio may also be associated with an index listing various acoustic properties or other descriptive information about the unit. Each unit includes an audio waveform corresponding with a phonetic unit, such as a short .wav file of the specific sound, along with a description of various features associated with the audio waveform. For example, an index entry for a particular unit may include information such as a particular unit's pitch, energy, duration, harmonics, center frequency, where the phonetic unit appears in a word, sentence, or phrase, the neighboring phonetic units, or the like. The unit selection engine1030may then use the information about each unit to select units to be joined together to form the speech output.

The unit selection engine1030matches the symbolic linguistic representation against information about the spoken audio units in the database. The unit database may include multiple examples of phonetic units to provide the system with many different options for concatenating units into speech. Matching units which are determined to have the desired acoustic qualities to create the desired output audio are selected and concatenated together (for example by a synthesis component1020) to form output audio data1090representing synthesized speech. Using all the information in the unit database, a unit selection engine1030may match units to the input text to select units that can form a natural sounding waveform. One benefit of unit selection is that, depending on the size of the database, a natural sounding speech output may be generated. As described above, the larger the unit database of the voice corpus, the more likely the system will be able to construct natural sounding speech.

In another method of synthesis—called parametric synthesis—parameters such as frequency, volume, noise, are varied by a parametric synthesis engine1032, digital signal processor or other audio generation device to create an artificial speech waveform output. Parametric synthesis uses a computerized voice generator, sometimes called a vocoder. Parametric synthesis may use an acoustic model and various statistical techniques to match a symbolic linguistic representation with desired output speech parameters. Using parametric synthesis, a computing system (for example, a synthesis component1020) can generate audio waveforms having the desired acoustic properties. Parametric synthesis may include the ability to be accurate at high processing speeds, as well as the ability to process speech without large databases associated with unit selection, but also may produce an output speech quality that may not match that of unit selection. Unit selection and parametric techniques may be performed individually or combined together and/or combined with other synthesis techniques to produce speech audio output.

The TTS component580may be configured to perform TTS processing in multiple languages. For each language, the TTS component580may include specially configured data, instructions and/or components to synthesize speech in the desired language(s). To improve performance, the TTS component580may revise/update the contents of the TTS unit storage1072based on feedback of the results of TTS processing, thus enabling the TTS component580to improve speech synthesis.

The TTS unit storage1072may be customized for an individual user based on his/her individualized desired speech output. In particular, the speech unit stored in a unit database may be taken from input audio data of the user speaking. For example, to create the customized speech output of the system, the system may be configured with multiple voice inventories1078a-1078n, where each unit database is configured with a different “voice” to match desired speech qualities. Such voice inventories may also be linked to user accounts. The voice selected by the TTS component580may be used to synthesize the speech. For example, one voice corpus may be stored to be used to synthesize whispered speech (or speech approximating whispered speech), another may be stored to be used to synthesize excited speech (or speech approximating excited speech), and so on. To create the different voice corpuses a multitude of TTS training utterances may be spoken by an individual (such as a voice actor) and recorded by the system. The audio associated with the TTS training utterances may then be split into small audio segments and stored as part of a voice corpus. The individual speaking the TTS training utterances may speak in different voice qualities to create the customized voice corpuses, for example the individual may whisper the training utterances, say them in an excited voice, and so on. Thus the audio of each customized voice corpus may match the respective desired speech quality. The customized voice inventory1078may then be used during runtime to perform unit selection to synthesize speech having a speech quality corresponding to the input speech quality.

Additionally, parametric synthesis may be used to synthesize speech with the desired speech quality. For parametric synthesis, parametric features may be configured that match the desired speech quality. If simulated excited speech was desired, parametric features may indicate an increased speech rate and/or pitch for the resulting speech. Many other examples are possible. The desired parametric features for particular speech qualities may be stored in a “voice” profile (e.g., parametric settings1068) and used for speech synthesis when the specific speech quality is desired. Customized voices may be created based on multiple desired speech qualities combined (for either unit selection or parametric synthesis). For example, one voice may be “shouted” while another voice may be “shouted and emphasized.” Many such combinations are possible.

Unit selection speech synthesis may be performed as follows. Unit selection includes a two-step process. First a unit selection engine1030determines what speech units to use and then it combines them so that the particular combined units match the desired phonemes and acoustic features and create the desired speech output. Units may be selected based on a cost function which represents how well particular units fit the speech segments to be synthesized. The cost function may represent a combination of different costs representing different aspects of how well a particular speech unit may work for a particular speech segment. For example, a target cost indicates how well an individual given speech unit matches the features of a desired speech output (e.g., pitch, prosody, etc.). A join cost represents how well a particular speech unit matches an adjacent speech unit (e.g., a speech unit appearing directly before or directly after the particular speech unit) for purposes of concatenating the speech units together in the eventual synthesized speech. The overall cost function is a combination of target cost, join cost, and other costs that may be determined by the unit selection engine1030. As part of unit selection, the unit selection engine1030chooses the speech unit with the lowest overall combined cost. For example, a speech unit with a very low target cost may not necessarily be selected if its join cost is high.

The system may be configured with one or more voice corpuses for unit selection. Each voice corpus may include a speech unit database. The speech unit database may be stored in TTS unit storage1072or in another storage component. For example, different unit selection databases may be stored in TTS unit storage1072. Each speech unit database (e.g., voice inventory) includes recorded speech utterances with the utterances' corresponding text aligned to the utterances. A speech unit database may include many hours of recorded speech (in the form of audio waveforms, feature vectors, or other formats), which may occupy a significant amount of storage. The unit samples in the speech unit database may be classified in a variety of ways including by phonetic unit (phoneme, diphone, word, etc.), linguistic prosodic label, acoustic feature sequence, speaker identity, etc. The sample utterances may be used to create mathematical models corresponding to desired audio output for particular speech units. When matching a symbolic linguistic representation the speech synthesis engine1018may attempt to select a unit in the speech unit database that most closely matches the input text (including both phonetic units and prosodic annotations). Generally the larger the voice corpus/speech unit database the better the speech synthesis may be achieved by virtue of the greater number of unit samples that may be selected to form the precise desired speech output.

Vocoder-based parametric speech synthesis may be performed as follows. A TTS component580may include an acoustic model, or other models, which may convert a symbolic linguistic representation into a synthetic acoustic waveform of the text input based on audio signal manipulation. The acoustic model includes rules which may be used by the parametric synthesis engine1032to assign specific audio waveform parameters to input phonetic units and/or prosodic annotations. The rules may be used to calculate a score representing a likelihood that a particular audio output parameter(s) (such as frequency, volume, etc.) corresponds to the portion of the input symbolic linguistic representation from the TTS front end1016.

The parametric synthesis engine1032may use a number of techniques to match speech to be synthesized with input phonetic units and/or prosodic annotations. One common technique is using Hidden Markov Models (HMMs). HMMs may be used to determine probabilities that audio output should match textual input. HMMs may be used to translate from parameters from the linguistic and acoustic space to the parameters to be used by a vocoder (the digital voice encoder) to artificially synthesize the desired speech. Using HMMs, a number of states are presented, in which the states together represent one or more potential acoustic parameters to be output to the vocoder and each state is associated with a model, such as a Gaussian mixture model. Transitions between states may also have an associated probability, representing a likelihood that a current state may be reached from a previous state. Sounds to be output may be represented as paths between states of the HMM and multiple paths may represent multiple possible audio matches for the same input text. Each portion of text may be represented by multiple potential states corresponding to different known pronunciations of phonemes and their parts (such as the phoneme identity, stress, accent, position, etc.). An initial determination of a probability of a potential phoneme may be associated with one state. As new text is processed by the speech synthesis engine1018, the state may change or stay the same, based on the processing of the new text. For example, the pronunciation of a previously processed word might change based on later processed words. A Viterbi algorithm may be used to find the most likely sequence of states based on the processed text. The HMMs may generate speech in parameterized form including parameters such as fundamental frequency (f0), noise envelope, spectral envelope, etc. that are translated by a vocoder into audio segments. The output parameters may be configured for particular vocoders such as a STRAIGHT vocoder, TANDEM-STRAIGHT vocoder, WORLD vocoder, HNM (harmonic plus noise) based vocoders, CELP (code-excited linear prediction) vocoders, GlottHMM vocoders, HSM (harmonic/stochastic model) vocoders, or others.

In addition to calculating potential states for one audio waveform as a potential match to a phonetic unit, the parametric synthesis engine1032may also calculate potential states for other potential audio outputs (such as various ways of pronouncing a particular phoneme or diphone) as potential acoustic matches for the acoustic unit. In this manner multiple states and state transition probabilities may be calculated.

The probable states and probable state transitions calculated by the parametric synthesis engine1032may lead to a number of potential audio output sequences. Based on the acoustic model and other potential models, the potential audio output sequences may be scored according to a confidence level of the parametric synthesis engine1032. The highest scoring audio output sequence, including a stream of parameters to be synthesized, may be chosen and digital signal processing may be performed by a vocoder or similar component to create an audio output including synthesized speech waveforms corresponding to the parameters of the highest scoring audio output sequence and, if the proper sequence was selected, also corresponding to the input text. The different parametric settings1068, which may represent acoustic settings matching a particular parametric “voice”, may be used by the synthesis component1020to ultimately create the output audio data1090.

When performing unit selection, after a unit is selected by the unit selection engine1030, the audio data corresponding to the unit may be passed to the synthesis component1020. The synthesis component1020may then process the audio data of the unit to create modified audio data where the modified audio data reflects a desired audio quality. The synthesis component1020may store a variety of operations that can convert unit audio data into modified audio data where different operations may be performed based on the desired audio effect (e.g., whispering, shouting, etc.).

As an example, input text may be received along with metadata, such as SSML tags, indicating that a selected portion of the input text should be whispered when output by the TTS module580. For each unit that corresponds to the selected portion, the synthesis component1020may process the audio data for that unit to create a modified unit audio data. The modified unit audio data may then be concatenated to form the output audio data1090. The modified unit audio data may also be concatenated with non-modified audio data depending on when the desired whispered speech starts and/or ends. While the modified audio data may be sufficient to imbue the output audio data with the desired audio qualities, other factors may also impact the ultimate output of audio such as playback speed, background effects, or the like, that may be outside the control of the TTS module580. In that case, other output data1085may be output along with the output audio data1090so that an ultimate playback device (e.g., device110) receives instructions for playback that can assist in creating the desired output audio. Thus, the other output data1085may include instructions or other data indicating playback device settings (such as volume, playback rate, etc.) or other data indicating how output audio data including synthesized speech should be output. For example, for whispered speech, the output audio data1090may include other output data1085that may include a prosody tag or other indicator that instructs the device110to slow down the playback of the output audio data1090, thus making the ultimate audio sound more like whispered speech, which is typically slower than normal speech. In another example, the other output data1085may include a volume tag that instructs the device110to output the speech at a volume level less than a current volume setting of the device110, thus improving the quiet whisper effect.

In order to apply the machine learning techniques, the machine learning processes themselves need to be trained. Training a machine learning component such as, in this case, one of the first or second models, requires establishing a “ground truth” for the training examples. In machine learning, the term “ground truth” refers to the accuracy of a training set's classification for supervised learning techniques. Various techniques may be used to train the models including backpropagation, statistical learning, supervised learning, semi-supervised learning, stochastic learning, or other known techniques.

While the device110may operate locally to a user (e.g., within a same environment so the device may receive inputs and playback outputs for the user) he server/system120may be located remotely from the device110as its operations may not require proximity to the user. The server/system120may be located in an entirely different location from the device110(for example, as part of a cloud computing system or the like) or may be located in a same environment as the device110but physically separated therefrom (for example a home server or similar device that resides in a user's home or business but perhaps in a closet, basement, attic, or the like). One benefit to the server/system120being in a user's home/business is that data used to process a command/return a response may be kept within the user's home, thus reducing potential privacy concerns.

Multiple systems (120/525) may be included in the overall system100of the present disclosure, such as one or more natural language processing systems120for performing ASR processing, one or more natural language processing systems120for performing NLU processing, one or more skill systems525, etc. In operation, each of these systems may include computer-readable and computer-executable instructions that reside on the respective device (120/525), as will be discussed further below.

Computer instructions for operating each device (110/120/525) and its various components may be executed by the respective device's controller(s)/processor(s) (1104/1204), using the memory (1106/1206) as temporary “working” storage at runtime. A device's computer instructions may be stored in a non-transitory manner in non-volatile memory (1106/1206), storage (1108/1208), or an external device(s). Alternatively, some or all of the executable instructions may be embedded in hardware or firmware on the respective device in addition to or instead of software.

Each device (110/120/525) includes input/output device interfaces (1102/1202). A variety of components may be connected through the input/output device interfaces (1102/1202), as will be discussed further below. Additionally, each device (110/120/525) may include an address/data bus (1124/1224) for conveying data among components of the respective device. Each component within a device (110/120/525) may also be directly connected to other components in addition to (or instead of) being connected to other components across the bus (1124/1224).

Referring toFIG.11, the device110may include input/output device interfaces1102that connect to a variety of components such as an audio output component such as a speaker112, a wired headset or a wireless headset (not illustrated), or other component capable of outputting audio. The device110may also include an audio capture component. The audio capture component may be, for example, a microphone114or array of microphones, a wired headset or a wireless headset (not illustrated), etc. If an array of microphones is included, approximate distance to a sound's point of origin may be determined by acoustic localization based on time and amplitude differences between sounds captured by different microphones of the array. The device110may additionally include a display116for displaying content. The device110may further include a camera1118.

The components of the device(s)110, the natural language command processing system120, or a skill system525may include their own dedicated processors, memory, and/or storage. Alternatively, one or more of the components of the device(s)110, the natural language command processing system120, or a skill system525may utilize the I/O interfaces (1102/1202), processor(s) (1104/1204), memory (1106/1206), and/or storage (1108/1208) of the device(s)110, natural language command processing system120, or the skill system525, respectively. Thus, the ASR component550may have its own I/O interface(s), processor(s), memory, and/or storage; the NLU component560may have its own I/O interface(s), processor(s), memory, and/or storage; and so forth for the various components discussed herein.

As noted above, multiple devices may be employed in a single system. In such a multi-device system, each of the devices may include different components for performing different aspects of the system's processing. The multiple devices may include overlapping components. The components of the device110, the natural language command processing system120, and a skill system525, as described herein, are illustrative, and may be located as a stand-alone device or may be included, in whole or in part, as a component of a larger device or system. As can be appreciated, a number of components may exist either on a system120and/or on device110. For example, the language processing components592(which may include ASR550), language output components593(which may include NLG579and TTS580), etc., for example as illustrated inFIGS.5and6. Unless expressly noted otherwise, the system version of such components may operate similarly to the device version of such components and thus the description of one version (e.g., the system version or the local version) applies to the description of the other version (e.g., the local version or system version) and vice-versa.

As illustrated inFIG.13, multiple devices (110a-110n,120,525) may contain components of the system and the devices may be connected over a network(s)199. The network(s)199may include a local or private network or may include a wide network such as the Internet. Devices may be connected to the network(s)199through either wired or wireless connections. For example, a speech-detection device110a, a smart phone110b, a smart watch110c, a tablet computer110d, a vehicle110e, a speech-detection device with display110f, a display/smart television110g, a washer/dryer110h, a refrigerator110i, a microwave110j, etc. (e.g., a device such as a FireTV stick, Echo Auto or the like) may be connected to the network(s)199through a wireless service provider, over a Wi-Fi or cellular network connection, or the like. Other devices are included as network-connected support devices, such as the natural language command processing system120, the skill system(s)525, and/or others. The support devices may connect to the network(s)199through a wired connection or wireless connection. Networked devices may capture audio using one-or-more built-in or connected microphones or other audio capture devices, with processing performed by ASR components, NLU components, or other components of the same device or another device connected via the network(s)199, such as the ASR component550, the NLU component560, etc. of the natural language command processing system120.

The above aspects of the present disclosure are meant to be illustrative. They were chosen to explain the principles and application of the disclosure and are not intended to be exhaustive or to limit the disclosure. Many modifications and variations of the disclosed aspects may be apparent to those of skill in the art. Persons having ordinary skill in the field of computers and speech processing should recognize that components and process steps described herein may be interchangeable with other components or steps, or combinations of components or steps, and still achieve the benefits and advantages of the present disclosure. Moreover, it should be apparent to one skilled in the art, that the disclosure may be practiced without some or all of the specific details and steps disclosed herein. Further, unless expressly stated to the contrary, features/operations/components, etc. from one embodiment discussed herein may be combined with features/operations/components, etc. from another embodiment discussed herein.