PATENT DOCUMENT

Publication Number: US-10354652-B2
Application Number: US-201816035513-A
Country: US
Kind Code: B2

Title: Applying neural network language models to weighted finite state transducers for automatic speech recognition

Abstract:
Systems and processes for converting speech-to-text are provided. In one example process, speech input can be received. A sequence of states and arcs of a weighted finite state transducer (WFST) can be traversed. A negating finite state transducer (FST) can be traversed. A virtual FST can be composed using a neural network language model and based on the sequence of states and arcs of the WFST. The one or more virtual states of the virtual FST can be traversed to determine a probability of a candidate word given one or more history candidate words. Text corresponding to the speech input can be determined based on the probability of the candidate word given the one or more history candidate words. An output can be provided based on the text corresponding to the speech input.

Claims:
What is claimed is: 
     
       1. A non-transitory computer-readable medium having instructions stored thereon; the instructions, when executed by one or more processors, cause the one or more processors to:
 receive speech input; 
 determine, based on the speech input and a weighted finite state transducer (WFST), a first probability of a candidate word given one or more history candidate words; 
 negate, using a negating finite state transducer (FST), the first probability of the candidate word given the one or more history candidate words; 
 compose a virtual FST using a neural network language model and based on the WFST, wherein one or more virtual states of the virtual FST represent the candidate word; 
 determine, using the virtual FST, a second probability of the candidate word given the one or more history candidate words; 
 determine, based on the WFST and the second probability of the candidate word given the one or more history candidate words, text corresponding to the speech input; 
 based on the determined text, perform one or more tasks to obtain a result; and 
 cause the result to be presented in spoken or visual form. 
 
     
     
       2. The non-transitory computer-readable medium of  claim 1 , wherein the WFST and the negating FST are composed prior to receiving the speech input, and wherein the virtual FST is composed after determining the first probability of the candidate word given the one or more history candidate words. 
     
     
       3. The non-transitory computer-readable medium of  claim 1 , wherein only one arc transitions out of each virtual state of the one or more virtual states of the virtual FST. 
     
     
       4. The non-transitory computer-readable medium of  claim 1 , wherein the instructions further cause the one or more processors to:
 determine, using the neural network language model, a third probability of the candidate word given the one or more history candidate words, wherein the virtual FST is composed using the third probability of the candidate word given the one or more history candidate words. 
 
     
     
       5. The non-transitory computer-readable medium of  claim 1 , wherein the WFST is a single finite state transducer composed from at least a third language model transducer, wherein the negating FST has a same structure and topology as the third language model transducer, and wherein scores associated with the negating FST are a negation of scores associated with the third language model transducer. 
     
     
       6. The non-transitory computer-readable medium of  claim 1 , wherein the WFST is composed with the negating FST, and wherein traversing the negating FST negates respective scores associated with the third language model transducer. 
     
     
       7. The non-transitory computer-readable medium of  claim 1 , wherein the one or more history candidate words correspond to a portion of the speech input, and wherein the instructions further cause the one or more processors to:
 determine, based on the WFST, a plurality of candidate words given the one or more history candidate words, wherein the virtual FST includes virtual states representing only the plurality of candidate words in the context of the one or more history candidate words. 
 
     
     
       8. A method for performing speech-to-text conversion, the method comprising:
 at an electronic device having a processor and memory:
 receiving speech input; 
 determining, based on the speech input and a weighted finite state transducer (WFST), a first probability of a candidate word given one or more history candidate words; 
 negating, using a negating finite state transducer (FST), the first probability of the candidate word given the one or more history candidate words; 
 composing a virtual FST using a neural network language model and based on the WFST, wherein one or more virtual states of the virtual FST represent the candidate word; 
 determining, using the virtual FST, a second probability of the candidate word given the one or more history candidate words; 
 determining, based on the WFST and the second probability of the candidate word given the one or more history candidate words, text corresponding to the speech input; 
 based on the determined text, performing one or more tasks to obtain a result; and 
 causing the result to be presented in spoken or visual form. 
 
 
     
     
       9. The method of  claim 8 , wherein the WFST and the negating FST are composed prior to receiving the speech input, and wherein the virtual FST is composed after determining the first probability of the candidate word given the one or more history candidate words. 
     
     
       10. The method of  claim 8 , wherein only one arc transitions out of each virtual state of the one or more virtual states of the virtual FST. 
     
     
       11. The method of  claim 8 , further comprising:
 determining, using the neural network language model, a third probability of the candidate word given the one or more history candidate words, wherein the virtual FST is composed using the third probability of the candidate word given the one or more history candidate words. 
 
     
     
       12. The method of  claim 8 , wherein the WFST is a single finite state transducer composed from at least a third language model transducer, wherein the negating FST has a same structure and topology as the third language model transducer, and wherein scores associated with the negating FST are a negation of scores associated with the third language model transducer. 
     
     
       13. The method of  claim 8 , wherein the WFST is composed with the negating FST, and wherein traversing the negating FST negates respective scores associated with the third language model transducer. 
     
     
       14. The method of  claim 8 , wherein the one or more history candidate words correspond to a portion of the speech input, and wherein the method further comprises:
 determining, based on the WFST, a plurality of candidate words given the one or more history candidate words, wherein the virtual FST includes virtual states representing only the plurality of candidate words in the context of the one or more history candidate words. 
 
     
     
       15. An electronic device, comprising:
 one or more processors; and 
 memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the one or more processors to:
 receive speech input; 
 determine, based on the speech input and a weighted finite state transducer (WFST), a first probability of a candidate word given one or more history candidate words; 
 negate, using a negating finite state transducer (FST), the first probability of the candidate word given the one or more history candidate words; 
 compose a virtual FST using a neural network language model and based on the WFST, wherein one or more virtual states of the virtual FST represent the candidate word; 
 determine, using the virtual FST, a second probability of the candidate word given the one or more history candidate words; 
 determine, based on the WFST and the second probability of the candidate word given the one or more history candidate words, text corresponding to the speech input; 
 based on the determined text, perform one or more tasks to obtain a result; and 
 cause the result to be presented in spoken or visual form. 
 
 
     
     
       16. The device of  claim 15 , wherein the WFST and the negating FST are composed prior to receiving the speech input, and wherein the virtual FST is composed after determining the first probability of the candidate word given the one or more history candidate words. 
     
     
       17. The device of  claim 15 , wherein only one arc transitions out of each virtual state of the one or more virtual states of the virtual FST. 
     
     
       18. The device of  claim 15 , wherein the instructions further cause the one or more processors to:
 determine, using the neural network language model, a third probability of the candidate word given the one or more history candidate words, wherein the virtual FST is composed using the third probability of the candidate word given the one or more history candidate words. 
 
     
     
       19. The device of  claim 15 , wherein the WFST is a single finite state transducer composed from at least a third language model transducer, wherein the negating FST has a same structure and topology as the third language model transducer, and wherein scores associated with the negating FST are a negation of scores associated with the third language model transducer. 
     
     
       20. The device of  claim 15 , wherein the WFST is composed with the negating FST, and wherein traversing the negating FST negates respective scores associated with the third language model transducer. 
     
     
       21. The device of  claim 15 , wherein the one or more history candidate words correspond to a portion of the speech input, and wherein the instructions further cause the one or more processors to:
 determine, based on the WFST, a plurality of candidate words given the one or more history candidate words, wherein the virtual FST includes virtual states representing only the plurality of candidate words in the context of the one or more history candidate words.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 15/156,161, which claims priority to U.S. Provisional Ser. No. 62/262,286, filed on Dec. 2, 2015, entitled APPLYING NEURAL NETWORK LANGUAGE MODELS TO WEIGHTED FINITE STATE TRANSDUCERS FOR AUTOMATIC SPEECH RECOGNITION, which is hereby incorporated by reference in its entirety for all purposes. This application also relates to the following co-pending applications: U.S. Non-Provisional patent application Ser. No. 14/494,305, “METHOD FOR SUPPORTING DYNAMIC GRAMMARS IN WFST-BASED ASR,” filed Sep. 23, 2014, which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     FIELD 
     The present disclosure relates generally to speech-to-text conversion, and more specifically to techniques for applying neural network language models to weighted finite state transducers for automatic speech recognition. 
     BACKGROUND 
     Language models can be implemented in automatic speech recognition (ASR) to predict the most probable current word (w) given one or more history words (h). Conventionally, statistical language models, such as n-gram language models, are applied in automatic speech recognition. Statistical language models are based on estimating conditional probabilities (e.g., probability of the current word given the one or more history words, P(w|h)) using training data, such as corpora of text. In order to achieve high recognition accuracy, the length of history words can be between two to four words (e.g., 3-gram to 5-gram). As the amount of language training data used in modern ASR systems is very large, the number of n-grams in n-gram language models can be very large. Large numbers of n-grams pose memory and speed problems in run-time ASR systems. Techniques such as pruning and cut-off have been implemented to control the actual number of n-grams in an n-gram language model. However, pruning and cut-off can reduce the accuracy of speech recognition. 
     BRIEF SUMMARY 
     Systems and processes for converting speech-to-text are provided. In one example process, speech input can be received. A sequence of states and arcs of a weighted finite state transducer (WFST) can be traversed based on the speech input. The sequence of states and arcs can represent one or more history candidate words and a current candidate word. A first probability of the candidate word given the one or more history candidate words can be determined by traversing the sequences of states and arcs of the WFST. A negating finite state transducer (FST) can be traversed, where traversing the negating FST can negate the first probability of the candidate word given the one or more history candidate words. A virtual FST can be composed using a neural network language model and based on the sequence of states and arcs of the WFST. One or more virtual states of the virtual FST can represent the current candidate word. The one or more virtual states of the virtual FST can be traversed, where a second probability of the candidate word given the one or more history candidate words is determined by traversing the one or more virtual states of the virtual FST. Text corresponding to the speech input can be determined based on the second probability of the candidate word given the one or more history candidate words. An output can be provided based on the text corresponding to the speech input. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures. 
         FIG. 1A  is a block diagram illustrating a portable multifunction device with a touch-sensitive display in accordance with some examples. 
         FIG. 1B  is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. 
         FIG. 2  illustrates a portable multifunction device having a touch screen in accordance with some embodiments. 
         FIG. 3  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. 
         FIGS. 4A and 4B  illustrate an exemplary user interface for a menu of applications on a portable multifunction device in accordance with some embodiments. 
         FIG. 5  illustrates an exemplary schematic block diagram of an automatic speech recognition module in accordance with some embodiments. 
         FIG. 6  illustrates an exemplary neural network language model in accordance with some embodiments. 
         FIGS. 7A-B  illustrate flow diagrams of an exemplary process for speech-to-text conversion in accordance with some embodiments. 
         FIGS. 8A-C  illustrate flow diagrams of an exemplary process for speech-to-text conversion in accordance with some embodiments. 
         FIG. 9  illustrates a functional block diagram of an electronic device in accordance with some embodiments. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     In the following description of the disclosure and embodiments, reference is made to the accompanying drawings in which it is shown by way of illustration of specific embodiments that can be practiced. It is to be understood that other embodiments and examples can be practiced and changes can be made without departing from the scope of the disclosure. 
     Techniques for applying neural network language models to weighted finite state transducers for automatic speech recognition are described herein. Neural network language models (NNLMs) map word indices to a continuous space and word probability distributions are estimated as smooth functions in that space. As a result, compared to n-gram language models, NNLMs provide better generalization for n-grams that are not found or are infrequently found in the training data. This enables greater recognition accuracy when NNLMs are implemented in automatic speech recognition. However, because NNLMs are generally configured to model the unabridged n-gram (e.g., for feedforward NNLMs) or the entire word history (e.g., for recurrent NNLMs), it can be difficult to efficiently integrate NNLMs into single pass WFST speech recognition decoder systems. In particular, NNLMs implemented in automatic speech recognition can be computationally expensive. 
     One approach to improving computational efficiency can be to first convert the NNLM into an intermediate form, such as an n-gram representation or a prefix tree representation of word sequences. The intermediate form can then be pruned or optimized before being integrated into the single pass WFST. However, the conversion process can require applying approximations, which can reduce the overall accuracy of the speech recognition system. The benefits of the NNLM are thus not fully experienced using this approach. Another approach to improving computation efficiency can be to implement a two-pass strategy where an n-gram language model is utilized to guide the initial decoding in the WFST. The NNLM can then be utilized during a second pass only to resolve ambiguities. However, the two-pass strategy can result in increased latency, which can negatively impact user experience for real-time applications. Further, because the NNLM is only utilized to resolve ambiguities, the benefits associated with the NNLM are not realized for every speech recognition pass. 
     Systems and processes for applying neural network language models to weighted finite state transducers for automatic speech recognition are described below. The exemplary systems and processes described herein can efficiently integrate an NNLM in a single pass WFST without sacrificing accuracy. In particular, the NNLM can be directly integrated with the WFST without converting the NNLM into an intermediate form (e.g., an n-gram or prefix tree representation). Further, the NNLM can be utilized during every decoding pass, rather than only during the rescoring pass in the latency time. 
     Embodiments of electronic devices, systems for speech-to-text conversion on such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as PDA and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other portable devices, such as laptops or tablet computers with touch-sensitive surfaces (e.g., touch screen displays and/or touch pads), may also be used. Exemplary embodiments of laptop and tablet computers include, without limitation, the iPad® and MacBook® devices from Apple Inc. of Cupertino, Calif. It should also be understood that, in some embodiments, the device is not a portable communications device, but is a desktop computer. Exemplary embodiments of desktop computers include, without limitation, the Mac Pro® from Apple Inc. of Cupertino, Calif. 
     In the discussion that follows, an electronic device that includes a display and a touch-sensitive surface is described. It should be understood, however, that the electronic device optionally includes one or more other physical user-interface devices, such as button(s), a physical keyboard, a mouse, and/or a joystick. 
     The device may support a variety of applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disk authoring application, a spreadsheet application, a gaming application, a telephone application, a video conferencing application, an e-mail application, an instant messaging application, a workout support application, a photo management application, a digital camera application, a digital video camera application, a web browsing application, a digital music player application, and/or a digital video player application. 
     The various applications that are executed on the device optionally use at least one common physical user-interface device, such as the touch-sensitive surface. One or more functions of the touch-sensitive surface as well as corresponding information displayed on the device are, optionally, adjusted and/or varied from one application to the next and/or within a respective application. In this way, a common physical architecture (such as the touch-sensitive surface) of the device optionally supports the variety of applications with user interfaces that are intuitive and transparent to the user. 
       FIGS. 1A and 1B  are block diagrams illustrating exemplary portable multifunction device  100  with touch-sensitive displays  112  in accordance with some embodiments. Touch-sensitive display  112  is sometimes called a “touch screen” for convenience. Device  100  may include memory  102 . Device  100  may include memory controller  122 , one or more processing units (CPU&#39;s)  120 , peripherals interface  118 , RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , input/output (I/O) subsystem  106 , other input or control devices  116 , and external port  124 . Device  100  may include one or more optical sensors  164 . Bus/signal lines  103  may allow these components to communicate with one another. Device  100  is one example of an electronic device that could be used to perform the techniques described herein. Specific implementations involving device  100  may have more or fewer components than shown, may combine two or more components, or may have a different configuration or arrangement of the components. The various components shown in  FIGS. 1A and 1B  may be implemented in hardware, software, or a combination of both. The components also can be implemented using one or more signal processing and/or application specific integrated circuits. 
     Memory  102  may include one or more computer readable storage mediums. The computer readable storage mediums may be tangible and non-transitory. Further, one or more computer readable storage mediums may include instructions for performing any of the methods or processes described herein. Memory  102  may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller  122  may control access to memory  102  by other components of device  100 . 
     Peripherals interface  118  can be used to couple input and output peripherals of the device to CPU  120  and memory  102 . The one or more processors  120  run or execute various software programs and/or sets of instructions stored in memory  102  to perform various functions for device  100  and to process data. In some embodiments, peripherals interface  118 , CPU  120 , and memory controller  122  may be implemented on a single chip, such as chip  104 . In some other embodiments, they may be implemented on separate chips. 
     RF (radio frequency) circuitry  108  receives and sends RF signals, also called electromagnetic signals. RF circuitry  108  converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry  108  may include well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry  108  may communicate with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The wireless communication may use any of a plurality of communications standards, protocols and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 502.11a, IEEE 502.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e-mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document. 
     Audio circuitry  110 , speaker  111 , and microphone  113  provide an audio interface between a user and device  100 . Audio circuitry  110  receives audio data from peripherals interface  118 , converts the audio data to an electrical signal, and transmits the electrical signal to speaker  111 . Speaker  111  converts the electrical signal to human-audible sound waves. Audio circuitry  110  also receives electrical signals converted by microphone  113  from sound waves. Audio circuitry  110  converts the electrical signal to audio data and transmits the audio data to peripherals interface  118  for processing. Audio data may be retrieved from and/or transmitted to memory  102  and/or RF circuitry  108  by peripherals interface  118 . In some embodiments, audio circuitry  110  also includes a headset jack (e.g.,  212 ,  FIG. 2 ). The headset jack provides an interface between audio circuitry  110  and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone). 
     I/O subsystem  106  couples input/output peripherals on device  100 , such as touch screen  112  and other input control devices  116 , to peripherals interface  118 . I/O subsystem  106  may include display controller  156  and one or more input controllers  160  for other input or control devices. The one or more input controllers  160  receive/send electrical signals from/to other input or control devices  116 . The other input control devices  116  may include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s)  160  may be coupled to any (or none) of the following: a keyboard, infrared port, USB port, and a pointer device such as a mouse. The one or more buttons (e.g.,  208 ,  FIG. 2 ) may include an up/down button for volume control of speaker  111  and/or microphone  113 . The one or more buttons may include a push button (e.g.,  206 ,  FIG. 2 ). A quick press of the push button may disengage a lock of touch screen  112  or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g.,  206 ) may turn power to device  100  on or off. The user may be able to customize a functionality of one or more of the buttons. Touch screen  112  is used to implement virtual or soft buttons and one or more soft keyboards. 
     Touch-sensitive display  112  provides an input interface and an output interface between the device and a user. Display controller  156  receives and/or sends electrical signals from/to touch screen  112 . Touch screen  112  displays visual output to the user. The visual output may include graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output may correspond to user-interface objects. 
     Touch screen  112  has a touch-sensitive surface, sensor or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen  112  and display controller  156  (along with any associated modules and/or sets of instructions in memory  102 ) detect contact (and any movement or breaking of the contact) on touch screen  112  and converts the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web-pages or images) that are displayed on touch screen  112 . In an exemplary embodiment, a point of contact between touch screen  112  and the user corresponds to a finger of the user. 
     Touch screen  112  may use LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. Touch screen  112  and display controller  156  may detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen  112 . In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif. 
     A touch-sensitive display in some embodiments of touch screen  112  may be analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen  112  displays visual output from device  100 , whereas touch sensitive touchpads do not provide visual output. 
     A touch-sensitive display in some embodiments of touch screen  112  may be as described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety. 
     Touch screen  112  may have a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user may make contact with touch screen  112  using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user. 
     In some embodiments, in addition to the touch screen, device  100  may include a touchpad (not shown) for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad may be a touch-sensitive surface that is separate from touch screen  112  or an extension of the touch-sensitive surface formed by the touch screen. 
     Device  100  also includes power system  162  for powering the various components. Power system  162  may include a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices. 
     Device  100  may also include one or more optical sensors  164 .  FIGS. 1A and 1B  show an optical sensor coupled to optical sensor controller  158  in I/O subsystem  106 . Optical sensor  164  may include charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor  164  receives light from the environment, projected through one or more lens, and converts the light to data representing an image. In conjunction with imaging module  143  (also called a camera module), optical sensor  164  may capture still images or video. In some embodiments, an optical sensor is located on the back of device  100 , opposite touch screen display  112  on the front of the device, so that the touch screen display may be used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user&#39;s image may be obtained for videoconferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor  164  can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor  164  may be used along with the touch screen display for both video conferencing and still and/or video image acquisition. 
     Device  100  may also include one or more proximity sensors  166 .  FIGS. 1A and 1B  show proximity sensor  166  coupled to peripherals interface  118 . Alternately, proximity sensor  166  may be coupled to input controller  160  in I/O subsystem  106 . Proximity sensor  166  may perform as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen  112  when the multifunction device is placed near the user&#39;s ear (e.g., when the user is making a phone call). 
     Device  100  optionally also includes one or more tactile output generators  167 .  FIG. 1A  shows a tactile output generator coupled to haptic feedback controller  161  in I/O subsystem  106 . Tactile output generator  167  optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor  165  receives tactile feedback generation instructions from haptic feedback module  133  and generates tactile outputs on device  100  that are capable of being sensed by a user of device  100 . In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system  112 ) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device  100 ) or laterally (e.g., back and forth in the same plane as a surface of device  100 ). In some embodiments, at least one tactile output generator sensor is located on the back of device  100 , opposite touch screen display  112 , which is located on the front of device  100 . 
     Device  100  may also include one or more accelerometers  168 .  FIGS. 1A and 1B  show accelerometer  168  coupled to peripherals interface  118 . Alternately, accelerometer  168  may be coupled to an input controller  160  in I/O subsystem  106 . Accelerometer  168  may perform as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device  100  optionally includes, in addition to accelerometer(s)  168 , a magnetometer (not shown) and a UPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device  100 . 
     In some embodiments, the software components stored in memory  102  include operating system  126 , communication module (or set of instructions)  128 , contact/motion module (or set of instructions)  130 , graphics module (or set of instructions)  132 , text input module (or set of instructions)  134 , Global Positioning System (GPS) module (or set of instructions)  135 , and applications (or sets of instructions)  136 . Furthermore, in some embodiments memory  102  stores device/global internal state  157 , as shown in  FIGS. 1A, 1B and 3 . Device/global internal state  157  includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display  112 ; sensor state, including information obtained from the device&#39;s various sensors and input control devices  116 ; and location information concerning the device&#39;s location and/or attitude. 
     Operating system  126  (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components. 
     Communication module  128  facilitates communication with other devices over one or more external ports  124  and also includes various software components for handling data received by RF circuitry  108  and/or external port  124 . External port  124  (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin connector that is the same as, or similar to and/or compatible with the 5-pin and/or 30-pin connectors used on devices made by Apple Inc. 
     Contact/motion module  130  may detect contact with touch screen  112  (in conjunction with display controller  156 ) and other touch sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module  130  includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module  130  receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, may include determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations may be applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module  130  and display controller  156  detects contact on a touchpad. In some embodiments, contact/motion module  130  and controller  160  detects contact on a click wheel. 
     Contact/motion module  130  may detect a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns. Thus, a gesture may be detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (lift off) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (lift off) event. 
     Graphics module  132  includes various known software components for rendering and displaying graphics on touch screen  112  or other display, including components for changing the intensity of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including without limitation text, web-pages, icons (such as user-interface objects including soft keys), digital images, videos, animations and the like. In some embodiments, graphics module  132  stores data representing graphics to be used. Each graphic may be assigned a corresponding code. Graphics module  132  receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller  156 . 
     Haptic feedback module  133  includes various software components for generating instructions used by tactile output generator(s)  167  to produce tactile outputs at one or more locations on device  100  in response to user interactions with device  100 . 
     Text input module  134 , which may be a component of graphics module  132 , provides soft keyboards for entering text in various applications (e.g., contacts  137 , e-mail  140 , IM  141 , browser  147 , and any other application that needs text input). 
     GPS module  135  determines the location of the device and provides this information for use in various applications (e.g., to telephone  138  for use in location-based dialing, to camera  143  as picture/video metadata, and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets). 
     Applications  136  may include the following modules (or sets of instructions), or a subset or superset thereof:
         Contacts module  137  (sometimes called an address book or contact list);   Telephone module  138 ;   Video conferencing module  139 ;   E-mail client module  140 ;   Instant messaging (IM) module  141 ;   Workout support module  142 ;   Camera module  143  for still and/or video images;   Image management module  144 ;   Video player module;   Music player module;   Browser module  147 ;   Calendar module  148 ;   Widget modules  149 , which may include one or more of: weather widget  149 - 1 , stocks widget  149 - 2 , calculator widget  149 - 3 , alarm clock widget  149 - 4 , dictionary widget  149 - 5 , and other widgets obtained by the user, as well as user-created widgets  149 - 6 ;   Widget creator module  150  for making user-created widgets  149 - 6 ;   Search module  151 ;   Video and music player module  152 , which merges video player module and music player module;   Notes module  153 ;   Map module  154 ; and/or   Online video module  155 .       

     Examples of other applications  136  that may be stored in memory  102  include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , contacts module  137  may be used to manage an address book or contact list (e.g., stored in application internal state  192  of contacts module  137  in memory  102  or memory  370 ), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone  138 , video conference module  139 , e-mail  140 , or IM  141 ; and so forth. 
     In conjunction with RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , telephone module  138  may be used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in address book  137 , modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation and disconnect or hang up when the conversation is completed. As noted above, the wireless communication may use any of a plurality of communications standards, protocols and technologies. 
     In conjunction with RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , touch screen  112 , display controller  156 , optical sensor  164 , optical sensor controller  158 , contact module  130 , graphics module  132 , text input module  134 , contacts module  137 , and telephone module  138 , video conference module  139  includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , e-mail client module  140  includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module  144 , e-mail client module  140  makes it very easy to create and send e-mails with still or video images taken with camera module  143 . 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact module  130 , graphics module  132 , and text input module  134 , the instant messaging module  141  includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages and to view received instant messages. In some embodiments, transmitted and/or received instant messages may include graphics, photos, audio files, video files and/or other attachments as are supported in a MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact module  130 , graphics module  132 , text input module  134 , GPS module  135 , map module  154 , and music player module, workout support module  142  includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store and transmit workout data. 
     In conjunction with touch screen  112 , display controller  156 , optical sensor(s)  164 , optical sensor controller  158 , contact/motion module  130 , graphics module  132 , and image management module  144 , camera module  143  includes executable instructions to capture still images or video (including a video stream) and store them into memory  102 , modify characteristics of a still image or video, or delete a still image or video from memory  102 . 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and camera module  143 , image management module  144  includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , and speaker  111 , video player module  145  includes executable instructions to display, present or otherwise play back videos (e.g., on touch screen  112  or on an external, connected display via external port  124 ). 
     In conjunction with touch screen  112 , display system controller  156 , contact module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , and browser module  147 , music player module  146  includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files. In some embodiments, device  100  may include the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , browser module  147  includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web-pages or portions thereof, as well as attachments and other files linked to web-pages. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , e-mail client module  140 , and browser module  147 , calendar module  148  includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to do lists, etc.) in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and browser module  147 , widget modules  149  are mini-applications that may be downloaded and used by a user (e.g., weather widget  149 - 1 , stocks widget  149 - 2 , calculator widget  149 - 3 , alarm clock widget  149 - 4 , and dictionary widget  149 - 5 ) or created by the user (e.g., user-created widget  149 - 6 ). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and browser module  147 , the widget creator module  150  may be used by a user to create widgets (e.g., turning a user-specified portion of a web-page into a widget). 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , search module  151  includes executable instructions to search for text, music, sound, image, video, and/or other files in memory  102  that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , and browser module  147 , video and music player module  152  includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen  112  or on an external, connected display via external port  124 ). In some embodiments, device  100  optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.). 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , notes module  153  includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , GPS module  135 , and browser module  147 , map module  154  may be used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions; data on stores and other points of interest at or near a particular location; and other location-based data) in accordance with user instructions. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , text input module  134 , e-mail client module  140 , and browser module  147 , online video module  155  includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port  124 ), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module  141 , rather than e-mail client module  140 , is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety. 
     Each of the above identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise rearranged in various embodiments. For example, video player module may be combined with music player module into a single module (e.g., video and music player module  152 ,  FIG. 1B ). In some embodiments, memory  102  may store a subset of the modules and data structures identified above. Furthermore, memory  102  may store additional modules and data structures not described above. 
     In some embodiments, memory  102  (or memory  370  of  FIG. 3 ) may store various user-specific data, such as, for example, user-specific vocabulary data, preference data, data from the user&#39;s electronic address book, user generated to-do lists, user generated shopping lists, or the like. 
     In some embodiments, device  100  is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device  100 , the number of physical input control devices (such as push buttons, dials, and the like) on device  100  may be reduced. 
     The predefined set of functions that may be performed exclusively through a touch screen and/or a touchpad include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device  100  to a main, home, or root menu from any user interface that may be displayed on device  100 . In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad. 
       FIG. 1B  is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory  102  (in  FIG. 1A ) or  370  ( FIG. 3 ) includes event sorter  170  (e.g., in operating system  126 ) and a respective application  136 - 1  (e.g., any of the aforementioned applications  137 - 151 ,  155 ,  380 - 390 ). 
     Event sorter  170  receives event information and determines the application  136 - 1  and application view  191  of application  136 - 1  to which to deliver the event information. Event sorter  170  includes event monitor  171  and event dispatcher module  174 . In some embodiments, application  136 - 1  includes application internal state  192 , which indicates the current application view(s) displayed on touch sensitive display  112  when the application is active or executing. In some embodiments, device/global internal state  157  is used by event sorter  170  to determine which application(s) is(are) currently active, and application internal state  192  is used by event sorter  170  to determine application views  191  to which to deliver event information. 
     In some embodiments, application internal state  192  includes additional information, such as one or more of: resume information to be used when application  136 - 1  resumes execution, user interface state information that indicates information being displayed or that is ready for display by application  136 - 1 , a state queue for enabling the user to go back to a prior state or view of application  136 - 1 , and a redo/undo queue of previous actions taken by the user. 
     Event monitor  171  receives event information from peripherals interface  118 . Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display  112 , as part of a multi-touch gesture). Peripherals interface  118  transmits information it receives from I/O subsystem  106  or a sensor, such as proximity sensor  166 , accelerometer(s)  168 , and/or microphone  113  (through audio circuitry  110 ). Information that peripherals interface  118  receives from I/O subsystem  106  includes information from touch-sensitive display  112  or a touch-sensitive surface. 
     In some embodiments, event monitor  171  sends requests to the peripherals interface  118  at predetermined intervals. In response, peripherals interface  118  transmits event information. In other embodiments, peripherals interface  118  transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration). In some embodiments, event sorter  170  also includes a hit view determination module  172  and/or an active event recognizer determination module  173 . 
     Hit view determination module  172  provides software procedures for determining where a sub-event has taken place within one or more views, when touch sensitive display  112  displays more than one view. Views are made up of controls and other elements that a user can see on the display. 
     Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected may correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected may be called the hit view, and the set of events that are recognized as proper inputs may be determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture. 
     Hit view determination module  172  receives information related to sub-events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module  172  identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module  172 , the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view. 
     Active event recognizer determination module  173  determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module  173  determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module  173  determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views. 
     Event dispatcher module  174  dispatches the event information to an event recognizer (e.g., event recognizer  180 ). In embodiments including active event recognizer determination module  173 , event dispatcher module  174  delivers the event information to an event recognizer determined by active event recognizer determination module  173 . In some embodiments, event dispatcher module  174  stores in an event queue the event information, which is retrieved by a respective event receiver  182 . 
     In some embodiments, operating system  126  includes event sorter  170 . Alternatively, application  136 - 1  includes event sorter  170 . In yet other embodiments, event sorter  170  is a stand-alone module, or a part of another module stored in memory  102 , such as contact/motion module  130 . 
     In some embodiments, application  136 - 1  includes a plurality of event handlers  190  and one or more application views  191 , each of which includes instructions for handling touch events that occur within a respective view of the application&#39;s user interface. Each application view  191  of the application  136 - 1  includes one or more event recognizers  180 . Typically, a respective application view  191  includes a plurality of event recognizers  180 . In other embodiments, one or more of event recognizers  180  are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application  136 - 1  inherits methods and other properties. In some embodiments, a respective event handler  190  includes one or more of: data updater  176 , object updater  177 , GUI updater  178 , and/or event data  179  received from event sorter  170 . Event handler  190  may utilize or call data updater  176 , object updater  177 , or GUI updater  178  to update the application internal state  192 . Alternatively, one or more of the application views  191  include one or more respective event handlers  190 . Also, in some embodiments, one or more of data updater  176 , object updater  177 , and GUI updater  178  are included in a respective application view  191 . 
     A respective event recognizer  180  receives event information (e.g., event data  179 ) from event sorter  170  and identifies an event from the event information. Event recognizer  180  includes event receiver  182  and event comparator  184 . In some embodiments, event recognizer  180  also includes at least a subset of: metadata  183 , and event delivery instructions  188  (which may include sub-event delivery instructions). 
     Event receiver  182  receives event information from event sorter  170 . The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch the event information may also include speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device. 
     Event comparator  184  compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub-event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator  184  includes event definitions  186 . Event definitions  186  contain definitions of events (e.g., predefined sequences of sub-events), for example, event  1  ( 187 - 1 ), event  2  ( 187 - 2 ), and others. In some embodiments, sub-events in an event ( 187 ) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event  1  ( 187 - 1 ) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event  2  ( 187 - 2 ) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display  112 , and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers  190 . 
     In some embodiments, event definitions  187  include a definition of an event for a respective user-interface object. In some embodiments, event comparator  184  performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display  112 , when a touch is detected on touch-sensitive display  112 , event comparator  184  performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler  190 , the event comparator uses the result of the hit test to determine which event handler  190  should be activated. For example, event comparator  184  selects an event handler associated with the sub-event and the object triggering the hit test. 
     In some embodiments, the definition for a respective event ( 187 ) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer&#39;s event type. 
     When a respective event recognizer  180  determines that the series of sub-events do not match any of the events in event definitions  186 , the respective event recognizer  180  enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture. 
     In some embodiments, a respective event recognizer  180  includes metadata  183  with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata  183  includes configurable properties, flags, and/or lists that indicate how event recognizers may interact, or are enabled to interact, with one another. In some embodiments, metadata  183  includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy. 
     In some embodiments, a respective event recognizer  180  activates event handler  190  associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer  180  delivers event information associated with the event to event handler  190 . Activating an event handler  190  is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer  180  throws a flag associated with the recognized event, and event handler  190  associated with the flag catches the flag and performs a predefined process. 
     In some embodiments, event delivery instructions  188  include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process. 
     In some embodiments, data updater  176  creates and updates data used in application  136 - 1 . For example, data updater  176  updates the telephone number used in contacts module  137 , or stores a video file used in video player module. In some embodiments, object updater  177  creates and updates objects used in application  136 - 1 . For example, object updater  177  creates a new user-interface object or updates the position of a user-interface object. GUI updater  178  updates the GUI. For example, GUI updater  178  prepares display information and sends it to graphics module  132  for display on a touch-sensitive display. 
     In some embodiments, event handler(s)  190  includes or has access to data updater  176 , object updater  177 , and GUI updater  178 . In some embodiments, data updater  176 , object updater  177 , and GUI updater  178  are included in a single module of a respective application  136 - 1  or application view  191 . In other embodiments, they are included in two or more software modules. 
     It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices  100  with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized. 
       FIG. 2  illustrates a portable multifunction device  100  having a touch screen  112  in accordance with some embodiments. The touch screen may display one or more graphics within user interface (UI)  200 . In this embodiment, as well as others described below, a user may select one or more of the graphics by making contact or touching the graphics, for example, with one or more fingers  202  (not drawn to scale in the figure) or one or more styluses  203  (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the contact may include a gesture, such as one or more taps, one or more swipes (from left to right, right to left, upward and/or downward) and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device  100 . In some embodiments, inadvertent contact with a graphic may not select the graphic. For example, a swipe gesture that sweeps over an application icon may not select the corresponding application when the gesture corresponding to selection is a tap. 
     Device  100  may also include one or more physical buttons, such as “home” or menu button  204 . As described previously, menu button  204  may be used to navigate to any application  136  in a set of applications that may be executed on device  100 . Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen  112 . 
     In one embodiment, device  100  includes touch screen  112 , menu button  204 , push button  206  for powering the device on/off and locking the device, volume adjustment button(s)  208 , Subscriber Identity Module (SIM) card slot  210 , head set jack  212 , and docking/charging external port  124 . Push button  206  may be used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device  100  also may accept verbal input for activation or deactivation of some functions through microphone  113 . 
       FIG. 3  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device  300  need not be portable. In some embodiments, device  300  is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child&#39;s learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device  300  typically includes one or more processing units (CPU&#39;s)  310 , one or more network or other communications interfaces  360 , memory  370 , and one or more communication buses  320  for interconnecting these components. Communication buses  320  may include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device  300  includes input/output (I/O) interface  330  comprising display  340 , which is typically a touch screen display. I/O interface  330  also may include a keyboard and/or mouse (or other pointing device)  350  and touchpad  355 . Memory  370  includes high-speed random access memory, such as DRAM, SRAM, DDR RAM or other random access solid state memory devices; and may include non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory  370  may optionally include one or more storage devices remotely located from CPU(s)  310 . In some embodiments, memory  370  stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory  102  of portable multifunction device  100  ( FIG. 1 ), or a subset thereof. Furthermore, memory  370  may store additional programs, modules, and data structures not present in memory  102  of portable multifunction device  100 . For example, memory  370  of device  300  may store drawing module  380 , presentation module  382 , word processing module  384 , website creation module  386 , disk authoring module  388 , and/or spreadsheet module  390 , while memory  102  of portable multifunction device  100  ( FIG. 1 ) may not store these modules. 
     Each of the above identified elements in  FIG. 3  may be stored in one or more of the previously mentioned memory devices. Each of the above identified modules corresponds to a set of instructions for performing a function described above. The above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. In some embodiments, memory  370  may store a subset of the modules and data structures identified above. Furthermore, memory  370  may store additional modules and data structures not described above. 
     Attention is now directed towards embodiments of user interfaces (“UI”) that may be implemented on portable multifunction device  100 .  FIG. 4A  illustrates exemplary user interfaces for a menu of applications on portable multifunction device  100  in accordance with some embodiments. Similar user interfaces may be implemented on device  300 . In some embodiments, user interface  400  includes the following elements, or a subset or superset thereof:
         Signal strength indicator(s)  402  for wireless communication(s), such as cellular and Wi-Fi signals;   Time  404 ;   Bluetooth indicator  405 ;   Battery status indicator  406 ;   Tray  408  with icons for frequently used applications, such as:
           Icon  416  for telephone module  138 , labeled “Phone,” which optionally includes an indicator  414  of the number of missed calls or voicemail messages;   Icon  418  for e-mail client module  140 , labeled “Mail,” which optionally includes an indicator  410  of the number of unread e-mails;   Icon  420  for browser module  147 , labeled “Browser;” and   Icon  422  for video and music player module  152 , also referred to as iPod (trademark of Apple Inc.) module  152 , labeled “iPod;” and   
           Icons for other applications, such as:
           Icon  424  for IM module  141 , labeled “Messages;”   Icon  426  for calendar module  148 , labeled “Calendar;”   Icon  428  for image management module  144 , labeled “Photos;”   Icon  430  for camera module  143 , labeled “Camera;”   Icon  432  for online video module  155 , labeled “Online Video;”   Icon  434  for stocks widget  149 - 2 , labeled “Stocks;”   Icon  436  for map module  154 , labeled “Maps;”   Icon  438  for weather widget  149 - 1 , labeled “Weather;”   Icon  440  for alarm clock widget  149 - 4 , labeled “Clock;”   Icon  442  for workout support module  142 , labeled “Workout Support;”   Icon  444  for notes module  153 , labeled “Notes;” and   Icon  446  for a settings application or module, labeled “Settings,” which provides access to settings for device  100  and its various applications  136 .   
               

       FIG. 4B  illustrates an exemplary user interface on a device (e.g., device  300 ,  FIG. 3 ) with a touch-sensitive surface  451  (e.g., a tablet or touchpad  355 ,  FIG. 3 ) that is separate from the display  450  (e.g., touch screen display  112 ). Although many of the examples which follow will be given with reference to inputs on touch screen display  112  (where the touch sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in  FIG. 4B . In some embodiments the touch sensitive surface (e.g.,  451 ) has a primary axis (e.g.,  452 ) that corresponds to a primary axis (e.g.,  453 ) on the display (e.g.,  450 ). In accordance with these embodiments, the device detects contacts (e.g.,  460  and  462 ) with the touch-sensitive surface  451  at locations that correspond to respective locations on the display (e.g.,  460  corresponds to  468  and  462  corresponds to  470 ). In this way, user inputs (e.g., contacts  460  and  462 , and movements thereof) detected by the device on the touch-sensitive surface (e.g.,  451 ) are used by the device to manipulate the user interface on the display (e.g.,  450 ) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods may be used for other user interfaces described herein. 
     Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously. 
     As used in the specification and claims, the term “open application” refers to a software application with retained state information (e.g., as part of device/global internal state  157  and/or application internal state  192 ). An open (e.g., executing) application is any one of the following types of applications:
         an active application, which is currently displayed on display  112  (or a corresponding application view is currently displayed on the display);   a background application (or background process), which is not currently displayed on display  112 , but one or more application processes (e.g., instructions) for the corresponding application are being processed by one or more processors  120  (i.e., running);   a suspended application, which is not currently running, and the application is stored in a volatile memory (e.g., DRAM, SRAM, DDR RAM, or other volatile random access solid state memory device of memory  102 ); and   a hibernated application, which is not running, and the application is stored in a non-volatile memory (e.g., one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices of memory  102 ).       

     As used herein, the term “closed application” refers to software applications without retained state information (e.g., state information for closed applications is not stored in a memory of the device). Accordingly, closing an application includes stopping and/or removing application processes for the application and removing state information for the application from the memory of the device. Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application. 
       FIG. 5  illustrates an exemplary schematic block diagram of ASR module  500  in accordance with some embodiments. In some embodiments, ASR module  500  can be implemented using one or more multifunction devices, including but not limited to devices  100 ,  400 , and  900  ( FIGS. 1A, 2, 4A -B, and  9 ). The multifunctional devices can include devices such as servers, personal computers, mobile device, or the like. In particular, ASR module  500  can be implemented in the memory (e.g., memory  102  or  370 ) and/or processor(s) (e.g., processor(s)  120  or  310 ) of one or more devices. ASR module  500  can enable speech recognition capabilities in a multifunctional device. In particular, ASR module  500  can be configured to perform any of the processes or methods described below (e.g., processes  700  and  800 ). 
     As shown in  FIG. 5 , ASR module  500  can include feature extractor  502  configured to process speech input and extract acoustic features from the speech input. In particular, feature extractor  502  can divide the speech input into a plurality of speech frames, each speech frame having a predetermined duration (e.g., 10 ms). Feature extractor  502  can further be configured to extract acoustic features (e.g., mel-frequency cepstral coefficients, etc.) from the plurality of audio frames. The acoustic features can be referred to as feature vectors. The acoustic features can represent various acoustic qualities of the speech input. 
     The extracted acoustic features can be received by recognition engine  504 , which can utilize one or more FSTs and/or language models to convert the acoustic features into text. In particular, recognition engine  504  can use WFST  506 , negating FST  508 , n-gram language model  510 , user-specific language model FST(s)  512 , virtual FST interface  514 , or NNLM  516  to convert the acoustic features into text. 
     WFST  506  can be a single optimized finite state transducer composed from various knowledge sources. In particular, WFST  506  can be composed from a Hidden Markov Model (HMM) topology, a context dependent phonetic model, a lexicon, and a language model. The combination of these knowledge sources can be optimized using conventional WFST optimization techniques, such as composition, determinization, or minimization. Decoding with WFST  506  using recognition engine  504  can thus be very efficient as a result of this optimization. In some examples, WFST  506  can be denoted as:
 
 HCLG   s =opt( H∘C∘L∘G   s )
 
where H is an HMM topology transducer, C is a context dependent phonetic model transducer, L is a lexicon tranducer, G s  is a language model transducer, and ∘ denotes the composition operation. The optimization operator opt( ) can perform, for example, epsilon-removal, weight pushing, composition, determinization, and minimization. In some examples, G s  can be an n-gram language model. In particular, G s  can be a limited sized n-gram language model in order for HCLG s  to be a compact and statistically optimized transducer. In some examples, G s  can be a unigram or a bigram language model.
 
     In some examples, WFST  506  can be configured to model a non-terminal class as a candidate word. As described in greater detail below, a non-terminal class can be a class of words (e.g., an entity class such as names of persons, places, applications, media, etc.). In some examples, the non-terminal class can be derived from user-specific data (e.g., names in a user&#39;s contact list). WFST  506  can be configured to model any number of non-terminal classes. 
     Negating FST  508  can have the same structure and topology as the language model transducer G s  from which WFST  506  is built, except that the scores (e.g., costs, likelihoods, or probabilities) are negated. Negating FST  508  can be denoted as G s   −1 . Negating FST  508  can be structured such that composing negating FST  508  with WFST  506  (HCLG s ) would negate the scores associated with the language model transducer G s . For example, if the weights of G s  are log probabilities, the weights of G s  are subtracted from HCLG s  when composing negating FST  508  with WFST  506 . Recognition engine  504  can be configured to compose the negating FST  508  with WFST  506 . 
     N-gram language model FST  510  can be a large vocabulary language model. In particular, n-gram language model FST  510  can have a greater number of n-grams than the language model G s  used to generate WFST  506 . N-gram language model FST  510  can be used by recognition engine  504  to supplement WFST  506 , thereby obtaining more accurate recognition results. 
     User-specific language model FST(s)  512  can include one or more language model FSTs that are generated using user-specific data. In particular, ASR module includes language model generator for generating user-specific language model FST(s)  512  using user-specific data. Language model generator  513  can be configured to receive or obtain user-specific data (e.g., block  802 , described below), such as user input, user usage data, user profile information, or the like. Language model generator  513  can further be configured to generate one or more user-specific language model FSTs  512  using the user-specific data (e.g., block  804 , described below). The one or more user-specific language model FSTs  512  can thus contain vocabulary and word sequences that are associated with a specific user of the electronic device. Each user-specific language model FST  512  can represent a particular non-terminal class. A non-terminal class can be a class of words. Examples of non-terminal classes can include names in a user&#39;s contact list ($ContactList) on the electronic device, names of applications ($AppList) in a user&#39;s electronic device, or names of places ($Place”) entered by a user on the electronic device. 
     Virtual FST interface  514  can be an interface for on-the-fly construction of one or more virtual FST using NNLM  516  and n-gram language model FST  510 . In particular, virtual FST interface  514  can encode the sequence of states and arcs traversed in WFST  506  by recognition engine  504 . Based on the sequence of states and arcs, virtual FST interface  514  can be configured to provide one or more history candidate words (h) to NNLM  516  or n-gram language model  510  and obtain a probability of a current candidate word given the one or more history candidate words (P(w|h)). Virtual FST interface  514  can thus enable NNLM  516  and/or n-gram language model FST  510  to be integrated with WFST  506  during run-time speech recognition. 
     NNLM  516  can be a multiple layer perceptron.  FIG. 6  illustrates exemplary neural NNLM  600  that can be similar or identical to NNLM  516 . NNLM  600  can include input layer  602 , output layer  604 , and one or more hidden layers  606  disposed between input layer  602  and output layer  604 . In this example, NNLM  600  includes three hidden layers  606 . It should be recognized, however, that in other examples, NNLM  600  can include any number of hidden layers  606 . 
     Each layer of NNLM  600  can include multiple units. The units can be the basic computational elements of NNLM  600  and can be referred to as dimensions, neurons, or nodes. As shown in  FIG. 6 , input layer  602  can include input units  608 , hidden layers  606  can include hidden units  610 , and output layer  604  can include output units  612 . Hidden layers  806  can each include any number of hidden units  810 . The units can be interconnected by connections  614 . Specifically, connections  614  can connect the units of one layer to the units of a subsequent layer. Further, each connection  614  can be associated with a weighting value and a bias followed by a nonlinear activation function. For simplicity, the weighting values and biases are not shown in  FIG. 6 . 
     Input layer  602  can represent a vocabulary table that maps one or more history candidate words (h) in a continuous space where each word is represented as a floating point vector. Input layer  602  can be configured to receive as inputs one or more history candidate words (h). In the present example, the one or more history candidate words include a first history word (w 1 ) and a second history word (w 2 ). Output layer  604  can be configured to estimate a probability distribution over the word to predict. In the present example, output layer  604  can be configured to output the probability of a third word (w 3 ) given the first history word (w 1 ) and the second history word (w 2 ). The number of output units  612  in output layer  604  can have the same number of neurons as the vocabulary size of NNLM  600 . Thus, output layer  604  can be configured to output a plurality of probabilities of numerous candidate words given the word history. It should be recognized that NNLM  600  can be a feedforward NNLM or a recurrent NNLM. 
     With reference back to  FIG. 5 , recognition engine  504  can function as a decoder. In particular, recognition engine  504  can perform decoding functions such as composing, interpolating, or traversing functions described below in processes  700  and  800 . For example, recognition engine  504  can traverse or compose one or more of WFST  506 , negating FST  508 , n-gram language model  510 , and user-specific language model FST(s)  512  to obtain speech recognition results. Further, recognition engine  504  can compose one or more virtual FSTs using one or more of WFST  506 , negating FST  508 , n-gram language model  510 , user-specific language model FST(s)  512 , virtual FST interface  514 , and NNLM  516 . Recognition engine  504  can then traverse the one or more virtual FSTs to obtain the probability of a current candidate word given one or more history candidate words (P(w|h)). 
       FIGS. 7A-B  and  8 A-C illustrate flow diagrams of exemplary processes  700  and  800  for speech-to-text conversion in accordance with some embodiments. More specifically, processes  700  and  800  can apply a neural network language model to weighted finite state transducers for speech-to-text conversion. Processes  700  or  800  can be performed at one or more of devices  100 ,  300 , and  900  ( FIGS. 1A, 2, 3A -B, and  9 ), described herein. In particular, processes  700  or  800  can be performed using an ASR module (e.g., ASR module  500  of  FIG. 5 ) implemented on one or more devices. It should be appreciated that some blocks in processes  700  or  800  can be combined, the order of some blocks can be changed, and some blocks can be omitted. 
     At block  702 , speech input can be received. The speech input can be received via a microphone (e.g., microphone  113 ) of an electronic device. The speech input can be in the form of an acoustic signal or an audio file. The speech input can include a user utterance, such as a voice command, dictation, request, authentication phrase, or the like. In some examples, the speech input can be pre-processed using a feature extractor (e.g., feature extractor  502 ) where the speech input is divided into a plurality of speech frames (e.g., 10 ms speech frames) and acoustic features (e.g., mel-frequency cepstral coefficients, etc.) are extracted from the plurality of segments. The acoustic features are thus a representation of the speech input. 
     At block  704 , a sequence of states and arcs of a WFST can be traversed based on the speech input (e.g., using the acoustic features of block  702 ). The WFST can be similar or identical to WFST  502  described above in  FIG. 5 . As described above, the WFST can be referred to as HCLG s . In particular, the WFST can be a single optimized finite state transducer composed from a Hidden Markov Model (HMM) topology (H), a context dependent phonetic model (C), a lexicon (L), and a language model (G s ). In some examples, the language model (G s ) from which the WFST is built can be a unigram language model or a bigram language model. The WFST can be a static finite state transducer built prior to receiving the speech input. 
     The sequence of states and arcs can represent one or more history candidate words (h) and a current candidate word (w). In some instances, the one or more history candidate words (h) can be referred to as context. In one example, the one or more history candidate words (h) can be “Let&#39;s go” and the current candidate word (w) can be “home.” By traversing the sequences of states and arcs of the WFST, a first probability of the candidate word given the one or more history candidate words (P 1 (w|h)) can be determined. 
     At block  706 , a negating FST can be composed with the WFST. The negating FST can be similar or identical to negating FST  508  described above. In particular, the negating FST can be a static FST built prior to receiving the speech input at block  702 . The negating FST can have the same structure as the language model transducer G s  from which the WFST is built, except the scores (e.g., costs, likelihoods, or probabilities) are negated. Composing the negating FST can be represented as follows:
 
 HCLG   s   ∘G   s   −1  
 
where HCLG s  denotes the WFST of block  704 , G s   −1  denotes the negating FST, and ∘ denotes the composition operation. In some examples, block  706  can be performed prior to block  708 . Further, in some examples, block  706  can be performed after block  704 .
 
     At block  708 , the negating FST (G s   −1 ) can be traversed. More specifically, the negating FST composed with the WFST can be traversed. The negating FST can be traversed after traversing the sequence of states and arcs of the WFST at block  704 . Traversing the negating FST can negate the first probability of the candidate word given the one or more history candidate words, P 1 (w|h). The negating FST can be a static finite state transducer built prior to receiving the speech input at block  702 . 
     At block  710 , a second probability of the candidate word given the one or more history candidate words (P 2 (w|h)) can be determined using a neural network language model (NNLM). The NNLM can be similar or identical to NNLMs  516  or  600 , described above. In some examples, the NNLM can be a feedforward NNLM. In other examples, the NNLM can be a recurrent NNLM. The NNLM can be more accurate than the language model (G s ) used to build the WFST of block  704 . In particular, the NNLM can be more accurate than the language model (G s ) in determining the probability of a word given a history of words. Further, in some examples, the NNLM can be more accurate than a higher order (e.g., 4-gram or greater) n-gram language model. 
     The second probability of the candidate word given the one or more history candidate words, P 2 (w|h) can be determined using a virtual FST interface (e.g., virtual FST interface  514 , described above). The virtual FST interface can encode the one or more history candidate words (h) and the current candidate word (w) traversed in the WFST. The one or more history candidate words (h) can be inputted into the NNLM (e.g., at input layer  602 ) using the virtual FST interface. Based on the input, the NNLM can output the probabilities of numerous candidate words given the one or more history candidate words (e.g., from output layer  604 ). The second probability of the current candidate word given the one or more history candidate words (P 2 (w|h)) can be obtained from among the outputted probabilities of numerous candidate words given the one or more history candidate words. 
     At block  712 , a virtual FST (e.g., virtual FST interface  514 ) can be composed using the NNLM of block  710  and based on the sequence of states and arcs of the WFST of block  704 . In particular, the virtual FST can be composed using the second probability of the candidate word given the one or more history candidate words (P 2 (w|h)) determined at block  710 . The virtual FST can be a virtual representation of the NNLM. In particular, the virtual FST can encode the one or more history candidate words (h) traversed in the WFST at block  704 . Further, the virtual FST can include one or more virtual states that represent the current candidate word with respect to the one or more history candidate words. Composing the virtual FST can be represented as follows:
 
 HCLG   s   ∘G   s   −1   ∘G   1   _   NNLM  
 
where G 1   _   NNLM  denotes the virtual FST. In some examples, the virtual FST can be deterministic where there is only one arc transitioning out of each of the one or more virtual states of the virtual FST. Further, the virtual FST can be composed on-the-fly, where the virtual FST is composed only after the sequence of states and arcs of the WFST are traversed. In particular, the virtual FST may not include states representing every candidate word outputting by the NNLM. Rather, the virtual FST can include virtual states representing only the current candidate words obtained from traversing the WFST at block  704 . The WFST thus guides the decoding. As a result, the computational and memory requirements can be reduced, which in turn reduces latency and improves user experience. Block  710  can be performed prior to block  712 .
 
     At block  714 , the one or more virtual states of the virtual FST can be traversed. The one or more virtual states of the virtual FST can encode a third probability of the candidate word given the one or more history candidate words (P 3 (w|h)). The third probability of the candidate word given the one or more history candidate words (P 3 (w|h)) can be determined based on the second probability of the candidate word given the one or more history candidate words (P 2 (w|h)). For example, the third probability of the candidate word given the one or more history candidate words (P 2 (w|h)) can be logarithmic representation of the second probability of the candidate word given the one or more history candidate words (P 2 (w|h)). By traversing the one or more virtual states of the virtual FST, the third probability of the candidate word given the one or more history candidate words (P 3 (w|h)) can be determined. 
     It should be appreciated that the virtual FST is composed and traversed for every speech recognition pass. Thus, the NNLM is utilized for every speech recognition pass and not merely implemented only for resolving ambiguities from the WFST. Further, it should be recognized that the NNLM is not initially converted into an intermediate form such as an n-gram representation or a prefix tree representation. Rather, the virtual FST is composed directly using the NNLM via the virtual FST interface. This preserves the accuracy advantages associated with the NNLM. At the same time, as described above, not all candidate word results from the NNLM are used to compose the virtual FST. Rather, the virtual FST can include virtual states representing only the current candidate words obtained from traversing the WFST at block  704 . Thus, process  700  enables the greater accuracy of the NNLM to be leveraged in speech recognition while limiting the computational requirements by utilizing the candidate word results from the WFST to guide the composition of the virtual FST. 
     At block  716 , text corresponding to the speech input can be determined based on the third probability of the candidate word given the one or more history candidate words (P 3 (w|h)). In particular, the one or more history candidate words (h) and the candidate word (w) can represent one candidate speech recognition result among a plurality of candidate speech recognition results. For example, the one or more history candidate words (h) and the candidate word (w) can represent the candidate speech recognition result “Let&#39;s go home.” The candidate speech result “Let&#39;s go home” can be associated with a probability based on the third probability of the candidate word given the one or more history candidate words (P 3 (w|h)). Other candidate speech recognition results can include “Let&#39;s go to Rome,” “Let&#39;s grow hope,” or the like. Each candidate speech recognition result can be associated with a probability. The plurality of candidate speech recognition results can be ranked according to their respective probabilities. The text corresponding to the speech input can be the candidate speech recognition result with the highest probability. 
     At block  718 , an output can be provided based on the text corresponding to the speech input. For example, if the text corresponding to the speech input is determined at block  716  to be “Let&#39;s go home,” the text can be displayed on the electronic device implementing process  700 . In another example, the speech input can be provided as a command to a digital assistant implemented on the electronic device. Based on the text “Let&#39;s go home,” the digital assistant can determine that the user wishes to obtain directions back home. In this example, the output can include map instructions on how to get home. It should be recognized that various other types of output can be provided based on the text corresponding to the speech input. 
     In some examples, the text can be determined using one or more additional language models. In these examples, blocks  712  and  714  can be repeated using one or more additional language models. For example, blocks  720 - 724  of  FIG. 7B  illustrate additional operations of process  700  that can be performed when the text corresponding to the speech input is determined using one or more additional language models. Blocks  720 - 724  can be performed prior to blocks  716  and  718 . 
     At block  720 , a second virtual FST can be composed using a second language model and based on the sequence of states and arcs. Block  720  can be similar to block  712  except that the second language model is different from the NNLM. For example, the second language models can be a large vocabulary n-gram language model or a second NNLM that is different from the NNLM of block  710 . The second virtual FST can encode the one or more history candidate words. Further, the second virtual FST can include one or more virtual states that represent the current candidate word. 
     In some examples, the second virtual FST can be deterministic where there is only one arc transitioning out of each of the one or more virtual states of the second virtual FST. Further, the virtual FST can be composed on-the-fly. In particular, the virtual FST can include virtual states representing only the current candidate words obtained from traversing the WFST at block  704 . 
     At block  722 , the one or more virtual states of the second virtual FST can be traversed. The one or more virtual states of the virtual FST can encode a fourth probability of the candidate word given the one or more history candidate words (P 4 (w|h)). The fourth probability of the candidate word given the one or more history candidate words can be derived using the second language model. By traversing the one or more virtual states of the second virtual FST, the fourth probability given the one or more history candidate words (P 4 (w|h)) can be determined. 
     At block  724 , the second probability of the candidate word given the one or more history candidate words (P 2 (w|h)) and the fourth probability of the candidate word given the one or more history candidate words (P 4 (w|h)) can be interpolated. The interpolation can be represented as follows:
 
 G   1   _   combined   ←G   1   _   NNLM ∘ +   G   1   _   2  
 
where G 1   _   NNLM  denotes the virtual FST, G 1   _   2  denotes the second virtual FST, and ∘ +  denotes the interpolation operation. A combined probability of the candidate word given the one or more history candidate words can be determined from the interpolation.
 
     The text corresponding to the speech input at block  714  can be determined based on the combined probability of the candidate word given the one or more history candidate words. The text can thus be determined based on both the second probability of the candidate word given the one or more history candidate words (P 2 (w|h)) and the fourth probability of the candidate word given the one or more history candidate words (P 4 (w|h)). By determining the text using two language models, the accuracy of the speech recognition is improved. It should be recognized that any number of language models can be utilized for speech recognition using the above described framework. For example, the interpolation of block  724  can be performed using any number of probabilities determined from any number of language models as follows:
 
 G   1   ←G   1   _   NNLM ∘ +   G   1   _   2 ∘ +    . . . G   1   _   (n−1) ∘ +   G   1   _   n  
 
where n is an integer and G 1   _   n  is the n th  virtual FST composed using the n th  language model.
 
     With reference to  FIGS. 8A-8C , process  800  depicts another exemplary process for speech-to-text conversion. Process  800  can be similar to process  700  except that the current candidate word can be modeled as a non-terminal class in the WFST. 
     At block  802 , user-specific data can be received. The user-specific data can be obtained from the memory (e.g., memory  102  or  370 ) of the electronic device  102 . In some examples, the user-specific data can be associated with a particular user profile on the electronic device. The user-specific data can include lists of words or word sequences associated with the user. In particular, the lists of words or word sequences can include entities associated with the user. The user-specific data can further include interaction frequencies associated with the words or word sequences. 
     In some examples, the user-specific usage data can include names found in a user&#39;s phonebook or contact list. In particular, input containing the contact information (e.g., names, numbers, addresses, etc.) can be received from the user in a variety of circumstances, such as in voice commands, voice dictation, emails, calls, messages, or the like. In some instances, the user&#39;s contact list can include names that may not be within the vocabulary of the automatic speech recognition system (e.g., ASR module  500 ). These out-of-vocabulary names can thus be received and used to provide recognition support for such user-specific words. 
     In some examples, the user-specific data can include names of applications on the electronic device (e.g., applications  136  on device  100 ). The names of the applications can be retrieved from the memory of the electronic device. Additionally, the names of the applications can be received from the user. For example, input containing the application names can be received from the user in a variety of circumstances, such as in voice commands to launch an application, close an application, direct instructions to an application, or the like. A user may also input application names when dictating emails, messages, or the like (e.g., recommending an application to a friend, posting to a social media feed the achievement of a new high score in a gaming application, or the like). In some instances, an application on a user device can have a name that may not be within the vocabulary of the automatic speech recognition system. The user-specific data can thus include a list of user applications to provide speech recognition support for such user-specific application names. 
     In some examples, the user-specific data can include names of media on the electronic device, media accessible to a user, or media otherwise associated with a user (e.g., media stored in memory on user device  102 , media available via streaming applications, media available via the Internet, media available from cloud storage, media available from a subscription service, etc.). Media names can include song tracks, music album titles, playlist names, genre names, mix names, artist names, radio station names, channel names, video titles, performer names, podcast titles, podcast producer names, or the like. For example, input containing media names can be received from the user in a variety of circumstances, such as in voice commands to play a song, play a video, tune to a radio station, play a mix of a particular genre of music, play an album, play an artist&#39;s music, or the like. A user may also input media names when dictating messages, searching for media, or the like (e.g., recommending an album to a friend, searching for a new song to buy, searching for a video clip to play, etc.). In some instances, media on a user device or available from other sources can have names that may not be within the vocabulary of the automatic speech recognition system. A list of media associated with a particular user can thus be received and used, as discussed in further detail below, to provide recognition support for such user specific media names. 
     In some examples, the user-specific data can include information regarding the frequency of interaction with the various entities. For example, the frequency of interaction can reflect the number of times a contact name, application name, or media name is received or selected as input on the electronic device. The frequency of interaction can include a ranking of entities with which the user interacts the most. Further, favorite lists, speed dial lists, or the like can be used to reflect a likely frequency of interaction between the user and various contacts. It should be understood that the frequency of interaction can be represented in any of a variety of ways (e.g., probabilities, percentages, rankings, interaction counts, number of interactions over a particular time period, etc.). 
     It should be appreciated that user-specific data can include a variety of other entities associated with a user that can be useful for ensuring speech recognition accuracy. For example, the user-specific data can include the names of locations, restaurants, or favorite foods associated with the user. Likewise, a variety of context information or other user-specific details can be received for speech recognition purposes. In some examples, such other entities and context information can be accompanied by interaction frequency data similar to that discussed above reflecting, for example, the likelihood that a particular entity will correspond to a user&#39;s similar-sounding utterance. Block  802  can be performed prior to receiving the speech input at block  806 . 
     At block  804 , a user-specific language model FST can be generated using the user-specific data. The user-specific language model FST can be a non-terminal language model FST that corresponds to a non-terminal class. In particular, the user-specific language model FST can be configured to determine the probability of one or more candidate words with respect to all possible candidate word sequences for the non-terminal class. The non-terminal class can correspond to any class of words. In some examples, the non-terminal class can correspond to a particular entity type such as, contact names, application names, media names, places, or the like. In one example, the user-specific data can include contact names from the user&#39;s contact list in the electronic device. In particular, the user-specific data can include the frequency of occurrence of a contact name in user input with respect to all contact names in the contact list. In this example, a user-specific language model FST representing the non-terminal class “$ContactList” can be generated using the user-specific data. The user-specific language model FST can thus be configured to determine the probability of a particular contact name with respect to all contact names in the user&#39;s contact list. The user-specific language model FST can be a static FST and can be generated at block  804  prior to receiving the speech input at block  806 . 
     At block  806 , speech input can be received. Block  806  can be similar or identical to block  702 , described above. 
     At block  808 , a sequence of states and arcs of a WFST can be traversed based on the speech input. Block  808  can be similar to block  704 , except that the current candidate word is modeled as a non-terminal class. The sequence of states and arcs can thus represent one or more history candidate words (h) and a non-terminal class (NT). In some examples, the non-terminal class represented by the sequence of states and arcs can be the non-terminal class corresponding to the user-specific language model FST generated at block  804 . By traversing the sequences of states and arcs of the WFST, a first probability of the non-terminal class given the one or more history candidate words (P 1 (NT|h)) can be determined. The non-terminal class can be a class type that represents a set of words or word sequences. One example of a non-terminal class can be “$FirstName,” which can include a set of words or word sequences corresponding to the names in the user&#39;s contact lists (e.g., Jon, Adam, Mary, Fred, Bob, Mary Jane, etc.). Another example of a non-terminal class can be “$Country,” which can include a set of words or word sequences corresponding to different countries (e.g., United States, Mexico, France, Germany, China, Korea, Japan, etc.). In some examples, the non-terminal class can be based on the user-specific data of block  802 . For example, the non-terminal class can be “$ContactList,” which can include the names in the user&#39;s contact list. 
     At block  810 , a negating finite state transducer (FST) can be composed with the WFST. Block  810  can be similar or identical to block  706 . In particular, the negating FST can be similar or identical to negating FST  508 , described above. The negating FST can be a static FST built prior to receiving the speech input at block  806 . The negating FST can have the same structure as the language model transducer G s  from which the WFST is built, except the scores (e.g., costs, likelihoods, or probabilities) are negated. 
     At block  812 , a negating FST can be traversed. Block  812  can be similar or identical to block  708 . In particular, traversing the negating FST can negate the first probability of the non-terminal class given the one or more history candidate words (P 1 (NT|h)). The negating FST can be a static finite state transducer built prior to receiving the speech input at block  806 . 
     At block  814 , a second probability of the non-terminal class given the one or more history candidate words (P 2 (NT|h)) can be determined using an NNLM. Block  814  can be similar to block  710 , described above. In particular, the NNLM can be similar or identical to NNLMs  516  or  600 , described above. In some examples, the NNLM can be a feedforward NNLM. In other examples, the NNLM can be a recurrent NNLM. The NNLM can be more accurate than the language model (G s ) used to build the WFST of block  808 . In some examples, the NNLM can be more accurate than a higher order (e.g., 4-gram or greater) n-gram language model. 
     At block  816 , a user-specific language model FST (G NT ) corresponding to the non-terminal class can be traversed. The user-specific language model FST (G NT ) can be a non-terminal language model FST. For example, the user-specific language model FST (G NT ) generated at block  804  can be traversed. By traversing the user-specific language model FST (G NT ), a probability of a current candidate word among a plurality of candidate words represented in the user-specific language model FST can be determined. Each of the plurality of candidate words represented in the user-specific language model FST can be associated with a non-terminal class. 
     At block  818 , a virtual FST (G 1 ) can be composed using the NNLM of block  814  and the user-specific language model FST (G NT ) of block  816 , and be based on the sequence of states and arcs of the WFST at block  808 . In particular, the virtual FST (G 1 ) can be composed by composing a virtual NNLM FST (G 1   _   NNLM ) with the user-specific language model FST (G NT ) as follows:
 
 G   1   ←G   1   _   NNLM ∘ ·   G   NT  
 
where ∘ ·  denotes the composition operation with respect to a non-terminal class. The virtual NNLM FST (G 1   _   NNLM ) can be a virtual representation of the NNLM and can be composed on-the fly in a similar manner as described in block  712 . In particular, the virtual NNLM FST (G 1   _   NNLM ) can be composed using the second probability of the non-terminal class given the one or more history candidate words (P 2 (NT|h)) determined at block  814 . The virtual NNLM FST (G 1   _   NNLM ) can encode the one or more history candidate words (h) traversed in the WFST at block  808 . Further, the virtual NNLM FST (G 1   _   NNLM ) can include one or more virtual states that represent the non-terminal class (NT) with respect to the one or more history candidate words (h).
 
     One or more virtual states of the virtual FST (G 1 ) can represent a current candidate word (w) corresponding to the non-terminal class (NT). For example, if the non-terminal class represents names in the user&#39;s contact list, the one or more virtual states of the virtual FST (G 1 ) can represent a current candidate word (w), such as “Bob,” “Joe,” or “Mike,” corresponding to a name in the user&#39;s contact list. The one or more virtual states of the virtual FST can be composed using phone-words from the WFST and based on the current candidate word (w) represented in the user-specific language model FST (G NT ). Phone-words can be words that represent phones. For example, if the current candidate word represented in the user-specific language model FST is “Mike” with a pronunciation of “M-AI-K,” it can be represented by phone-words “M”, “AI”, and “K.” The WFST can be modified to generate these phone-words and the one or more virtual states of the virtual FST can be composed using these phone-words. Further, the virtual FST can be composed using the probability of the current candidate word (w) among the plurality of candidate words represented in the user-specific language model FST (block  816 ). The virtual FST (G 1 ) can be deterministic where only one arc transitions out of each virtual state of the one or more virtual states of the virtual FST. 
     It should be appreciated that in some examples, more than one user-specific language model FST can be implemented. In particular, each user-specific language model FST can represent a different non-terminal class. In these examples, the virtual FST (G 1 ) can be composed with the user-specific language model FSTs as follows:
 
 G   1   ←G   1   _   NNLM ∘ · ( G   NT1   , . . . ,G   NTn )
 
where n is an integer, and G NT1 , . . . , G NTn  denotes n different user-specific language model FSTs.
 
     At block  820 , the one or more virtual states of the virtual FST can be traversed. The one or more virtual states of the virtual FST can encode the probability of the candidate word given the one or more history words and the non-terminal class (P(w|h,NT). The probability of the candidate word given the one or more history words and the non-terminal class (P(w|h,NT) can be based on the second probability of the non-terminal class given the one or more history candidate words (P 2 (NT|h) and the probability of the current candidate word (w) among the plurality of candidate words represented in the user-specific language model FST (block  816 ). By traversing the virtual state of the virtual FST, a probability of the current candidate word given the one or more history candidate words and the non-terminal class (P 1 (w|h,NT)) can be determined. 
     It should be recognized that in some examples, the one or more virtual states can represent two or more current candidate words corresponding to the non-terminal class. For example, the non-terminal class “$ContactList” can include names having more than one word (e.g., “Mary Jane,” “Bob Jones,” “Joe Black,” or “Mike Jordon Smith”). In these examples, the probability of two or more current candidate words given the one or more history candidate words and the non-terminal class (P 1  (w|h,NT)) can be determined. 
     At block  822 , text corresponding to the speech input can be determined based on the probability of the current candidate word given the one or more history candidate words and the non-terminal class (P 1 (w|h,NT)). Block  822  can be similar to block  716 , described above. 
     At block  824 , an output based on the text corresponding to the speech input can be provided. Block  824  can be similar or identical to block  718 , described above. 
     It should be recognized that, in some examples, the text can be determined using one or more additional language models. In these examples, blocks  818  and  820  can be repeated using one or more additional language models. For example, blocks  826 - 830  of  FIG. 8C  illustrate additional operations of process  800  that can be performed when the text corresponding to the speech input is determined using one or more additional language models. Blocks  826 - 830  can be performed prior to blocks  822  and  824 . 
     At block  826 , a second virtual FST can be composed using a second language model and the user-specific language model FST and be based on the sequence of states and arcs of the WFST. Block  826  can be similar to block  818 , except that a different language model is used. One or more virtual states of the second virtual FST can represent the current candidate word (w) corresponding to the non-terminal class (NT). In some examples, the second language model can be a second NNLM that is different from the NNLM of block  814 . In these examples, the second virtual FST (G 1   _   2 ) can be composed by composing a virtual NNLM FST (G 1   _   NNLM2 ) with the user-specific language model FST (G NT ) as follows:
 
 G   1   _   2   ←G   1   _   NNLM2 ∘ ·   G   NT  
 
where ∘ denotes the composition operation with respect to a non-terminal class. In other examples, the second language model can be a large vocabulary n-gram language model. In particular, the second language model can be a higher order (e.g., 4-gram or greater) n-gram language model. In these examples, the second virtual FST (G 1   _   2 ) can be composed by composing a virtual n-gram FST (G n-gram ) with the user-specific language model FST (G NT ) as follows:
 
 G   1-2   ←G   1   _   n-gram  replace  G   NT  
 
where “replace” denotes the replace FST operation with respect to an n-gram language model FST (G 1   _   n-gram ).
 
     At block  828 , the one or more virtual states of the second virtual FST can be traversed. Block  828  can be similar to block  820 , described above. In particular, the one or more virtual states can represent a current candidate word (w) with respect to the one or more history candidate words (h) and the non-terminal state (NT). The current candidate word (w) can be obtained from the user-specific language model FST. The one or more virtual states can encode the second probability of the current candidate word given the one or more history candidate words and the non-terminal class (P 2 (w|h,NT)). By traversing the one or more virtual states of the second virtual FST, the second probability of the current candidate word given the one or more history candidate words and the non-terminal class (P 2 (w|h,NT)) can be determined. 
     At block  830 , the probability of the current candidate word given the one or more history candidate words and the non-terminal class (P 1 (w|h,NT)) from block  820  and the second probability of the current candidate word given the one or more history candidate words and the non-terminal class (P 2 (w|h,NT)) from block  828  can be interpolated. The interpolation can be represented as follows:
 
 G   1   _   combined   ←G   1   _   NNLM ∘ +   G   1   _   2  
 
where G 1   _   NNLM  denotes the virtual FST, G 1   _   2  denotes the second virtual FST, and ∘ +  denotes the interpolation operation. A combined probability of the candidate word given the one or more history candidate words can be determined from the interpolation.
 
     The text corresponding to the speech input at block  822  can be determined based on the combined probability of the candidate word given the one or more history candidate words and the non-terminal class. The text is thus determined based on both the probability of the candidate word given the one or more history candidate words and the non-terminal class (P 1 (w|h, NT)) and the second probability of the candidate word given the one or more history candidate words and the non-terminal class (P 2 (w|h, NT)). By determining the text using two language models, the accuracy of the speech recognition is improved. As described above, it should be recognized that any number of language models can be utilized for speech recognition using the above described framework. The combined probability can thus be based on any number of language models. 
     In accordance with some embodiments,  FIG. 9  shows a functional block diagram of an electronic device  900  configured in accordance with the principles of the various described embodiments, including those described with reference to  FIG. 6 . The functional blocks of the device are, optionally, implemented by hardware, software, or a combination of hardware and software to carry out the principles of the various described embodiments. It is understood by persons of skill in the art that the functional blocks described in  FIG. 9  are, optionally, combined or separated into sub-blocks to implement the principles of the various described embodiments. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein. 
     As shown in  FIG. 9 , electronic device  900  can include display unit  902  configured to display text or user interface objects, and audio receiving unit  904  configured to receive speech input, and input unit  906  configured to receive user-specific data. Electronic device  900  can further include processing unit  908  coupled to display unit  902  and audio receiving unit  904 . In some examples, processing unit  908  can include traversing unit  910 , composing unit  912 , determining unit  914 , providing unit  916 , interpolating unit  918 , receiving unit  920 , and determining unit  916 . 
     In accordance with some embodiments, processing unit  908  is configured to traverse (e.g., with traversing unit  910 ), based on the speech input, a sequence of states and arcs of a weighted finite state transducer (WFST). The sequence of states and arcs represents one or more history candidate words and a current candidate word. A first probability of the candidate word given the one or more history candidate words is determined by traversing the sequences of states and arcs of the WFST. Processing unit  908  is further configured to traverse (e.g., with traversing unit  910 ) a negating finite state transducer (FST). Traversing the negating FST negates the first probability of the candidate word given the one or more history candidate words. Processing unit  908  is further configured to compose (e.g., with composing unit  912 ) a virtual FST using a neural network language model and based on the sequence of states and arcs of the WFST, where one or more virtual states of the virtual FST represent the current candidate word. Processing unit  908  is further configured to traverse (e.g., with traversing unit  910 ) the one or more virtual states of the virtual FST. A second probability of the candidate word given the one or more history candidate words is determined by traversing the one or more virtual states of the virtual FST. Processing unit  908  is further configured to determine (e.g., with determining unit  914 ), based on the second probability of the candidate word given the one or more history candidate words, text corresponding to the speech input. Processing unit  908  is further configured to provide (e.g., with providing unit  916 ) an output based on the text corresponding to the speech input. 
     In some examples, the virtual FST is composed after traversing the sequence of states and arcs of the WFST. 
     In some examples, only one arc transitions out of each virtual state of the one or more virtual states of the virtual FST. 
     In some examples, processing unit  908  is further configured to determine (e.g., with determining unit  914 ), using the neural network language model, a third probability of the candidate word given the one or more history candidate words. The virtual FST is composed using the third probability of the candidate word given the one or more history candidate words. 
     In some examples, processing unit  908  is further configured to compose (e.g., with composing unit  912 ) a second virtual FST using a second language model and based on the sequence of states and arcs, where one or more virtual states of the second virtual FST represents the current candidate word. Processing unit  908  is further configured to traverse (e.g., with traversing unit  910 ) the one or more virtual states of the second virtual FST, where a fourth probability of the candidate word given the one or more history candidate words is determined by traversing the one or more virtual states of the second virtual FST, and where the text corresponding to the speech input is determined based on the fourth probability of the candidate word given the one or more history candidate words. 
     In some examples, processing unit  908  is further configured to interpolate (e.g., with interpolating unit  918 ) the second probability of the candidate word given the one or more history candidate words and the fourth probability of the candidate word given the one or more history candidate words. A combined probability of the candidate word given the one or more history candidate words is determined by the interpolating. The text corresponding to the speech input is determined based on the combined probability of the candidate word given the one or more history candidate words. 
     In some examples, the second language model is an n-gram language model. 
     In some examples, processing unit  908  is further configured to compose (e.g., with composing unit  912 ) the negating FST with the WFST prior to traversing the negating FST. 
     In some examples, the virtual FST is composed prior to traversing the one or more virtual states of the virtual FST. 
     In some examples, the WFST is a static finite state transducer built prior to receiving the speech input. In some examples, the negating FST is a static finite state transducer built prior to receiving the speech input. In some examples, the WFST is a single finite state transducer composed from a Hidden Markov Model (HMM) topology, a context dependent phonetic model, a lexicon, and a language model. In some examples, the language model is a unigram language model or a bigram language model. In some examples, the neural network language model is more accurate than the language model. In some examples, the neural network language model is a feedforward neural network language model. In some examples, the neural network language model is a recurrent neural network language model. 
     In accordance with some embodiments, processing unit  908  is configured to traverse (e.g., with traversing unit  901 ), based on the speech input, a sequence of states and arcs of a weighted finite state transducer (WFST). The sequence of states and arcs represents one or more history candidate words and a non-terminal class and a first probability of the non-terminal class given the one or more history candidate words is determined by traversing the sequences of states and arcs of the WFST. Processing unit  908  is further configured to traverse (e.g., with traversing unit  910 ) a negating finite state transducer (FST), where traversing the negating FST negates the first probability of the non-terminal class given the one or more history candidate words. Processing unit  908  is further configured to compose (e.g., with composing unit  912 ) a virtual FST using a neural network language model and a user-specific language model FST, and based on the sequence of states and arcs of the WFST. One or more virtual states of the virtual FST represent a current candidate word corresponding to the non-terminal class. Processing unit  908  is further configured to traverse (e.g., with traversing  910 ) the one or more virtual states of the virtual FST, where a probability of the current candidate word given the one or more history candidate words and the non-terminal class is determined by traversing the one or more virtual states of the virtual FST. Processing unit  908  is further configured to determine (e.g., with determining unit  914 ), based on the probability of the current candidate word given the one or more history candidate words and the non-terminal class, text corresponding to the speech input. Processing unit  908  is further configured to provide (e.g., with providing unit  916 ) an output based on the text corresponding to the speech input. 
     In some examples, processing unit  908  is further configured to determine (e.g., with determining unit  914 ), using the neural network language model, a second probability of the non-terminal class given the one or more history candidate words. The virtual FST is composed using the second probability of the non-terminal class given the one or more history candidate words. 
     In some examples, processing unit  908  is further configured to traverse (e.g., with traversing unit  910 ) the user-specific language model FST, where a probability of the current candidate word among a plurality of candidate words represented in the user-specific language model FST is determined by traversing the user-specific language model FST. The virtual FST is composed using the probability of the current candidate word among the plurality of candidate words represented in the user-specific language model FST. 
     In some examples, the one or more virtual states of the virtual FST are composed using phone-word units from the WFST and based on the current candidate word represented in the user-specific language model FST. 
     In some examples, processing unit  908  is further configured to, prior to receiving the speech input, receive (e.g., with receiving unit  920  and via input unit  906 ) user-specific data and generate (e.g., with generating unit  922 ) the user-specific language model FST using the user-specific data. 
     In some examples, only one arc transitions out of each virtual state of the one or more virtual states of the virtual FST. 
     In some examples, processing unit  908  is further configured to compose (e.g., with composing unit  912 ) a second virtual FST using a second language model and the user-specific language model FST, and based on the sequence of states and arcs of the WFST, where one or more virtual states of the second virtual FST represent the current candidate word corresponding to the non-terminal class. Processing unit  908  is further configured to traverse (e.g., with traversing unit  910 ) the one or more virtual states of the second virtual FST, where a second probability of the current candidate word given the one or more history candidate words and the non-terminal class is determined by traversing the one or more virtual states of the second virtual FST. The text corresponding to the speech input is determined based on the second probability of the current candidate word given the one or more history candidate words and the non-terminal class. 
     In some examples, the second language model is an n-gram language model. In some examples, the second language model is a second neural network language model. 
     In some examples, processing unit  908  is further configured to interpolate (e.g., with interpolating unit  918 ) 1) the probability of the current candidate word given the one or more history candidate words and the non-terminal class and 2) the second probability of the current candidate word given the one or more history candidate words and the non-terminal class. A combined probability of the current candidate word given the one or more history candidate words and the non-terminal class is obtained by the interpolating. The text corresponding to the speech input is determined based on the combined probability of the current candidate word given the one or more history candidate words and the non-terminal class. 
     In some examples, the virtual FST is composed after traversing the sequence of states and arcs of the WFST. 
     In some examples, processing unit  908  is further configured to compose (e.g., with composing unit  912 ) the negating FST with the WFST prior to traversing the negating FST. 
     In some examples, the WFST is a static finite state transducer built prior to receiving the speech input. In some examples, the negating FST is a static finite state transducer built prior to receiving the speech input. In some examples, the WFST is a single finite state transducer composed from a Hidden Markov Model (HMM) topology, a context dependent phonetic model, a lexicon, and a language model. In some examples, the language model is a unigram language model or a bigram language model. In some examples, the neural network language model is more accurate than the language model. In some examples, the neural network language model is a feedforward neural network language model. In some examples, the neural network language model is a recurrent neural network language model. 
     The operation described above with respect to  FIGS. 7A-B  and  8 A-C are, optionally, implemented by components depicted in  FIGS. 1A-B ,  3 ,  5 , and  9 . For example, receiving operations ( 702 ,  806 ) can be implemented by microphone  113 , audio circuitry  110 , and/or processor(s)  120 . Traversing operations ( 704 ,  708 ,  714 ,  722 ,  808 ,  812 ,  816 ,  820 ), composing operations ( 706 ,  712 ,  720 ,  810 ,  818 ,  826 ), determining operations ( 710 ,  716 ,  814 ,  822 ), interpolating operations ( 724 ,  830 ), receiving operation ( 802 ), and generating operation  804  can be implemented by automatic speech recognition module  500 . It would be clear to a person of ordinary skill in the art how other processes can be implemented based on the components depicted in  FIGS. 1A-B ,  3 ,  5 , and  9 . 
     It is understood by persons of skill in the art that the functional blocks described in  FIG. 9  are, optionally, combined or separated into sub-blocks to implement the principles of the various described embodiments. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein. For example, processing unit  908  can have an associated “controller” unit that is operatively coupled with processing unit  908  to enable operation. This controller unit is not separately illustrated in  FIG. 9  but is understood to be within the grasp of one of ordinary skill in the art who is designing a device having a processing unit  908 , such as device  900 . As another example, one or more units, such as audio receiving unit  904 , may be hardware units outside of processing unit  908  in some embodiments. The description herein thus optionally supports combination, separation, and/or further definition of the functional blocks described herein. 
     In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein. 
     Although the above description uses terms first, second, etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first probability could be termed a second probability, and, similarly, a second probability could be termed a first probability, without departing from the scope of the present invention. The first probability and the second probability are both probabilities, but they are not the same probability. 
     The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a”, “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “in accordance with determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     As described above, one aspect of the present technology is the gathering and use of data available from various sources to generate language models and thus improve the accuracy of speech recognition. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, home addresses, or any other identifying information. 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to generate user-specific language models. Accordingly, use of such personal information data enables more accurate speech recognition. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. 
     The present disclosure further contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. For example, personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection should occur only after receiving the informed consent of the users. Additionally, such entities would take any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. 
     Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. In another example, users can select not to provide location information for targeted content delivery services. In yet another example, users can select to not provide precise location information, but permit the transfer of location zone information. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, content can be selected and delivered to users by inferring preferences based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to the content delivery services, or publically available information. 
     Although the disclosure and examples have been fully described with reference to the accompanying figures, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the appended claims.

Metadata:
Filing Date: 20180713
Publication Date: 20190716
Grant Date: 20190716
Priority Date: 20151202
Inventors: HUANG, RONGQING
OPARIN, ILYA
Assignee: APPLE INC
CPC Classifications: [{"code": "G10L15/193", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/16", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/197", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/285", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L15/142", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L2015/085", "inventive": false, "first": false, "tree": "[]"}, {"code": "G10L15/197", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L15/193", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/193", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/16", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/142", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/285", "inventive": true, "first": true, "tree": "[]"}, {"code": "G10L15/197", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L15/16", "inventive": true, "first": false, "tree": "[]"}, {"code": "G10L2015/085", "inventive": false, "first": false, "tree": "[]"}]
Family ID: 58798484