Deliberation by Text-Only and Semi-Supervised Training

A method of text-only and semi-supervised training for deliberation includes receiving training data including unspoken textual utterances that are each not paired with any corresponding spoken utterance of non-synthetic speech, and training a deliberation model that includes a text encoder and a deliberation decoder on the unspoken textual utterances. The method also includes receiving, at the trained deliberation model, first-pass hypotheses and non-causal acoustic embeddings. The first-pass hypotheses is generated by a recurrent neural network-transducer (RNN-T) decoder for the non-causal acoustic embeddings encoded by a non-causal encoder. The method also includes encoding, using the text encoder, the first-pass hypotheses generated by the RNN-T decoder, and generating, using the deliberation decoder attending to both the first-pass hypotheses and the non-causal acoustic embeddings, second-pass hypotheses.

TECHNICAL FIELD

This disclosure relates to deliberation by text-only and semi-supervised training.

BACKGROUND

Modern automated speech recognition (ASR) systems focus on providing not only high quality (e.g., a low word error rate (WER)), but also low latency (e.g., a short delay between the user speaking and a transcription appearing). Moreover, when using an ASR system today there is a demand that the ASR system decode utterances in a streaming fashion that corresponds to real-time or even faster than real-time. To illustrate, when an ASR system is deployed on a mobile phone that experiences direct user interactivity, an application on the mobile phone using the ASR system may require the speech recognition to be streaming such that words appear on the screen as soon as they are spoken. Here, it is also likely that the user of the mobile phone has a low tolerance for latency. Due to this low tolerance, the speech recognition strives to run on the mobile device in a manner that minimizes an impact from latency and inaccuracy that may detrimentally affect the user's experience.

SUMMARY

One aspect of the disclosure provides a computer-implemented method that when executed on data processing hardware causes the data processing hardware to perform operations that include receiving training data including unspoken textual utterances that are each not paired with any corresponding spoken utterance of non-synthetic speech, and training a deliberation model that includes a text encoder and a deliberation decoder on the unspoken textual utterances. The operations also include receiving, at the trained deliberation model, first-pass hypotheses and non-causal acoustic embeddings. The first-pass hypotheses is generated by a recurrent neural network-transducer (RNN-T) decoder for the non-causal acoustic embeddings encoded by a non-causal encoder. The operations also include encoding, using the text encoder, the first-pass hypotheses generated by the RNN-T decoder, and generating, using the deliberation decoder attending to both the first-pass hypotheses and the non-causal acoustic embeddings, second-pass hypotheses.

Implementations of the disclosure may include one or more of the following optional features. In some implementations, the operations further include receiving a sequence of acoustic frames and encoding, using a casual encoder, each acoustic frame in the sequence of acoustic frames into a corresponding causal acoustic embedding. The operations also include generating, using the non-causal encoder configured to receive the encoded causal acoustic embeddings as input, the non-causal acoustic embeddings, and decoding, using the RNN-T decoder, the non-causal acoustic embeddings to generate the first-pass hypotheses. In some examples, the deliberation decoder includes a long short-term memory (LSTM) network followed by a softmax layer. Here, the LSTM network may include at least two layers. In some implementations, the text encoder includes a stack of self-attention blocks each having a multi-headed self-attention mechanism. In these implementations, each self-attention block may include one of a Conformer block or a Transformer block.

In some examples, training the deliberation model includes pre-training the text encoder on each unspoken textual utterance by tokenizing the corresponding unspoken textual utterance into a sequence of sub-word units. This example also includes replacing each tokenized sub-word unit in a first portion of the tokenized sequence of sub-word units with a mask token, and replacing each token sub-word unit in a second portion of the tokenized sequence of sub-word units with a random token. Additionally or alternatively, training the deliberation model includes generating, using a text-to-speech model, a corresponding synthetic speech representation for each unspoken textual utterance of the received training data, and training the deliberation model using the unspoken textual utterances and corresponding synthetic speech representations.

In some implementations, the training data further includes un-transcribed non-synthetic speech utterances, each un-transcribed non-synthetic speech utterance not paired with a corresponding transcription. In these implementations, the operations further include predicting, using a trained speech recognition model, the corresponding transcription for each un-transcribed non-synthetic speech utterance. Here, training the deliberation model further includes training the deliberation decoder using the un-transcribed non-synthetic speech utterances and the corresponding predicted transcriptions as semi-supervised data. In some examples, generating the second-pass hypotheses includes generating, using a first attention mechanism attending to the encoded first-pass hypotheses, first context vectors, generating, using a second attention mechanism attending to the non-causal acoustic embeddings, second context vectors, and decoding the first context vectors and the second context vectors at the deliberation decoder to form the second-pass hypotheses.

Another aspect of the disclosure provides a system including data processing hardware and memory hardware in communication with the data processing hardware. The memory hardware stores instructions that when executed by the data processing hardware cause the data processing hardware to perform operations that include receiving training data including unspoken textual utterances that are each not paired with any corresponding spoken utterance of non-synthetic speech, and training a deliberation model that includes a text encoder and a deliberation decoder on the unspoken textual utterances. The operations also include receiving, at the trained deliberation model, first-pass hypotheses and non-causal acoustic embeddings. The first-pass hypotheses is generated by a recurrent neural network-transducer (RNN-T) decoder for the non-causal acoustic embeddings encoded by a non-causal encoder. The operations also include encoding, using the text encoder, the first-pass hypotheses generated by the RNN-T decoder, and generating, using the deliberation decoder attending to both the first-pass hypotheses and the non-causal acoustic embeddings, second-pass hypotheses.

This aspect may include one or more of the following optional features. In some implementations, the operations further include receiving a sequence of acoustic frames and encoding, using a casual encoder, each acoustic frame in the sequence of acoustic frames into a corresponding causal acoustic embedding. The operations also include generating, using the non-causal encoder configured to receive the encoded causal acoustic embeddings as input, the non-causal acoustic embeddings, and decoding, using the RNN-T decoder, the non-causal acoustic embeddings to generate the first-pass hypotheses. In some examples, the deliberation decoder includes a long short-term memory (LSTM) network followed by a softmax layer. Here, the LSTM network may include at least two layers. In some implementations, the text encoder includes a stack of self-attention blocks each having a multi-headed self-attention mechanism. In these implementations, each self-attention block may include one of a Conformer block or a Transformer block.

In some examples, training the deliberation model includes pre-training the text encoder on each unspoken textual utterance by tokenizing the corresponding unspoken textual utterance into a sequence of sub-word units. This example also includes replacing each tokenized sub-word unit in a first portion of the tokenized sequence of sub-word units with a mask token, and replacing each token sub-word unit in a second portion of the tokenized sequence of sub-word units with a random token. Additionally or alternatively, training the deliberation model includes generating, using a text-to-speech model, a corresponding synthetic speech representation for each unspoken textual utterance of the received training data, and training the deliberation model using the unspoken textual utterances and corresponding synthetic speech representations.

In some implementations, the training data further includes un-transcribed non-synthetic speech utterances, each un-transcribed non-synthetic speech utterance not paired with a corresponding transcription. In these implementations, the operations further include predicting, using a trained speech recognition model, the corresponding transcription for each un-transcribed non-synthetic speech utterance. Here, training the deliberation model further includes training the deliberation decoder using the un-transcribed non-synthetic speech utterances and the corresponding predicted transcriptions as semi-supervised data. In some examples, generating the second-pass hypotheses includes generating, using a first attention mechanism attending to the encoded first-pass hypotheses, first context vectors, generating, using a second attention mechanism attending to the non-causal acoustic embeddings, second context vectors, and decoding the first context vectors and the second context vectors at the deliberation decoder to form the second-pass hypotheses.

DETAILED DESCRIPTION

Speech recognition continues to evolve to meet the untethered and the nimble demands of a mobile environment. New speech recognition architectures or improvements to existing architectures continue to be developed that seek to increase the quality of automatic speech recognition systems (ASR). To illustrate, speech recognition initially employed multiple models where each model had a dedicated purpose. For instance, an ASR system included an acoustic model (AM), a pronunciation model (PM), and a language model (LM). The acoustic model mapped segments of audio (i.e., frames of audio) to phonemes. The pronunciation model connected these phonemes together to form words while the language model was used to express the likelihood of given phrases (i.e., the probability of a sequence of words). Although these individual models worked together, each model was trained independently and often manually designed on different datasets.

The approach of separate models enables a speech recognition system to be fairly accurate, especially when the training corpus (i.e., body of training data) for a given model caters to the effectiveness of the model. However, the need to independently train separate models introduced its own complexities and led to an architecture with integrated models. These integrated models sought to use a single neural network to directly map an audio waveform (i.e., input sequence) to an output sentence (i.e., output sequence). This resulted in a sequence-to-sequence approach, which generated a sequence of words (or graphemes) when given a sequence of audio features. Examples of sequence-to-sequence models include “attention-based” models and “listen-attend-spell” (LAS) models. A LAS model transcribes speech utterances into characters using a listener component, an attender component, and a speller component. Here, the listener is a recurrent neural network (RNN) encoder that receives an audio input (e.g., a time-frequency representation of speech input) and maps the audio input to a higher-level feature representation. The attender attends to the higher-level feature to learn an alignment between input features and predicted subword units (e.g., a grapheme or a wordpiece). The speller is an attention-based RNN decoder that generates character sequences from the input by producing a probability distribution over a set of hypothesized words. With an integrated structure, all components of a model may be trained jointly as a single end-to-end (E2E) neural network. Here, an E2E model refers to a model whose architecture is constructed entirely of a neural network. A fully neural network functions without external and/or manually designed components (e.g., finite state transducers, a lexicon, or text normalization modules). Additionally, when training E2E models, these models generally do not require bootstrapping from decision trees or time alignments from a separate system.

Although early E2E models proved accurate and a training improvement over individually trained models, these E2E models, such as the LAS model, functioned by reviewing an entire input sequence before generating output text, and thus, did not allow streaming outputs as inputs were received. Without streaming capabilities, an LAS model is unable to perform real-time voice transcription. Due to this deficiency, deploying the LAS model for speech applications that are latency sensitive and/or require real-time voice transcription may pose issues. This makes an LAS model alone not an ideal model for mobile technology (e.g., mobile phones) that often relies on real-time applications (e.g., real-time communication applications).

Additionally, speech recognition systems that have acoustic, pronunciation, and language models, or such models composed together, may rely on a decoder that has to search a relatively large search graph associated with these models. With a large search graph, it is not conducive to host this type of speech recognition system entirely on-device. Here, when a speech recognition system is hosted “on-device,” a device that receives the audio input uses its processor(s) to execute the functionality of the speech recognition system. For instance, when a speech recognition system is hosted entirely on-device, the processors of the device do not need to coordinate with any off-device computing resources to perform the functionality of the speech recognition system. A device that performs speech recognition not entirely on-device relies on remote computing (e.g., of a remote computing system or cloud computing) and therefore online connectivity to perform at least some function of the speech recognition system. For example, a speech recognition system performs decoding with a large search graph using a network connection with a server-based model.

Unfortunately, being reliant upon a remote connection makes a speech recognition system vulnerable to latency issues and/or inherent unreliability of communication networks. To improve the usefulness of speech recognition by avoiding these issues, speech recognition systems have again evolved into a form of a sequence-to-sequence model known as a recurrent neural network transducer (RNN-T). A RNN-T does not employ an attention mechanism and, unlike other sequence-to-sequence models that generally need to process an entire sequence (e.g., audio waveform) to produce an output (e.g., a sentence), the RNN-T continuously processes input samples and streams output symbols, a feature that is particularly attractive for real-time communication. For instance, speech recognition with an RNN-T may output characters one-by-one as spoken. Here, an RNN-T uses a feedback loop that feeds symbols predicted by the model back into itself to predict the next symbols. Because decoding the RNN-T includes a beam search through a single neural network instead of a large decoder graph, an RNN-T may scale to a fraction of the size of a server-based speech recognition model. With the size reduction, the RNN-T may be deployed entirely on-device and able to run offline (i.e., without a network connection); therefore, avoiding unreliability issues with communication networks.

In addition to speech recognition systems operating with low latency, a speech recognition system also needs to be accurate at recognizing speech. Often for models that perform speech recognition, a metric that may define an accuracy of a model is a word error rate (WER). A WER refers to a measure of how many words are changed compared to a number of words actually spoken. Commonly, these word changes refer to substitutions (i.e., when a word gets replaced), insertions (i.e., when a word is added), and/or deletions (i.e., when a word is omitted). To illustrate, a speaker says “car,” but an ASR system transcribes the word “car” as “bar.” This is an example of a substitution due to phonetic similarity. When measuring the capability of an ASR system compared to other ASR systems, the WER may indicate some measure of improvement or quality capability relative to another system or some baseline.

Although an RNN-T model showed promise as a strong candidate model for on-device speech recognition, the RNN-T model alone still lags behind a large state-of-the-art conventional model (e.g., a server-based model with separate AM, PM, and LMs) in terms of quality (e.g., speech recognition accuracy). Yet a non-streaming E2E, LAS model has speech recognition quality that is comparable to large state-of-the-art conventional models. In a two-pass model, a non-streaming LAS model, for example, rescores streamed hypotheses from a first-pass. This second-pass LAS model approach attends to acoustics in order to rescore hypotheses. In contrast, an alternative method known as a class of neural correction model uses text instead of acoustics to generate hypotheses. In other words, there are different variables that may be attended to in order to refine a hypothesis in a second-pass. As such, the model proposed herein is a variation on the RNN-T/LAS two-pass model. This variant uses a deliberation network that combines acoustics and first-pass text hypotheses for the second pass of the two-pass model.

To improve on the quality of voice search, implementations herein are directed toward pre-training (e.g., as shown inFIGS.3A-3C) the second pass of the deliberation network using text-only data in a masked language model. In addition to using text-only data, unlabeled audio utterances are used for semi-supervised training of the text encoder. By incorporating text-only data in pre-training the deliberation network, joint acoustic and text decoder training, and semi-supervised training in a single model, this pre-trained two-pass deliberation network may become more accurate than a large conventional speech recognition model. For instance, in some tests, the pre-trained two-pass deliberation network has achieved a 4.1% voice search performance improvement and near 12% long-tail WER reduction when compared to an untrained two-pass deliberation network, and 8% relative WER reduction when compared to a large convention recognition model.

FIGS.1A and1Bare example systems100a,100bincluding a speech environment100in which a user's10manner of interacting with a computing device, such as a user device110, may be through voice input. The user device110(also referred to generally as a device110) is configured to capture sounds (e.g., streaming audio data) from one or more users10within the speech-enabled environment. Here, the streaming audio data12may refer to a spoken utterance by the user10that functions as an audible query, a command for the device110, or an audible communication captured by the device110. Speech-enabled systems of the device110may field the query or the command by answering the query and/or causing the command to be performed.

The user device110may correspond to any computing device associated with a user10and capable of receiving audio data12. Some examples of user devices110include, but are not limited to, mobile devices (e.g., mobile phones, tablets, laptops, etc.), computers, wearable devices (e.g., smart watches), smart appliances, internet of things (IoT) devices, smart speakers, etc. The user device110includes data processing hardware112and memory hardware114in communication with the data processing hardware112and storing instructions, that when executed by the data processing hardware112, cause the data processing hardware112to perform one or more operations. The user device110further includes an audio subsystem116with an audio capture device (e.g., microphone)116,116afor capturing and converting spoken utterances12within the speech-enabled system100into electrical signals and a speech output device (e.g., a speaker)116,116bfor communicating an audible audio signal (e.g., as output audio data from the device110). While the user device110implements a single audio capture device116ain the example shown, the user device110may implement an array of audio capture devices116awithout departing from the scope of the present disclosure, whereby one or more capture devices116ain the array may not physically reside on the user device110, but be in communication with the audio subsystem116. The user device110is further configured to perform speech recognition processing on the streaming audio data12using a speech recognizer200. The speech recognizer200(also referred to as the model200) resides on the user device110(e.g., hardware110,112) of the user10and/or on a remote computing device60(e.g., one or more remote servers of a distributed system executing in a cloud-computing environment) in communication with the user device10via a network40. In some examples, the audio subsystem116of the user device110that includes the audio capture device116ais configured to receive audio data12(e.g., spoken utterances) and to convert the audio data12into a digital format compatible with the speech recognizer200. The digital format may correspond to acoustic frames (e.g., parameterized acoustic frames), such as mel frames. For instance, the parameterized acoustic frames correspond to log-mel filterbank energies.

In some examples, such asFIG.1A, the user10interacts with a program or application118of the user device110that uses the speech recognizer200. For instance,FIG.1Adepicts the user10communicating with an automated assistant application. In this example, the user10asks the automated assistant, “What time is the concert tonight?” This question from the user10is a spoken utterance12captured by the audio capture device116aand processed by audio subsystems116of the user device110. In this example, the speech recognizer200of the user device110receives the audio input202(e.g., as acoustic frames) of “what time is the concert tonight” and transcribes the audio input202into a transcription204(e.g., a text representation of “what time is the concert tonight?”). Here, the automated assistant of the application118may respond to the question posed by the user10using natural language processing. Natural language processing generally refers to a process of interpreting written language (e.g., the transcription204) and determining whether the written language prompts any action. In this example, the automated assistant uses natural language processing to recognize that the question from the user10regards the user's schedule and more particularly a concert on the user's schedule. By recognizing these details with natural language processing, the automated assistant returns a response to the user's query where the response states, “Doors open at 8:30 pm for the concert tonight.” In some configurations, natural language processing may occur on a remote system in communication with the data processing hardware112of the user device110.

FIG.1Bis another example of speech recognition with the speech recognizer200. In this example, the user10associated with the user device110is communicating with a friend named Jane Doe with a communication application118. Here, the user10named Ted, communicates with Jane by having the speech recognizer200transcribe his voice inputs. The audio capture device116captures these voice inputs and communicates them in a digital form (e.g., acoustic frames) to the speech recognizer200. The speech recognizer200transcribes these acoustic frames into text that is sent to Jane via the communication application118. Because this type of application118communicates via text, the transcription204from the speech recognizer200may be sent to Jane without further processing (e.g., natural language processing).

In some examples, such asFIG.2, the speech recognizer200is configured in an enhanced two-pass architecture having a first pass206followed by a second pass208. Generally speaking, the two-pass architecture of the speech recognizer200includes a first encoder210(e.g., a causal encoder210), a second encoder220(e.g., a non-causal encoder220), an RNN-T decoder230, and a deliberation model240. In two-pass decoding, the second pass208may improve the initial outputs from the first pass206with techniques such as lattice rescoring or n-best re-ranking. In other words, the RNN-T decoder230produces streaming predictions and the deliberation model240finalizes the prediction. Here, specifically, the deliberation model240rescores streamed hypotheses232yRfrom the RNN-T decoder230. Although it is generally discussed that the deliberation model240functions in a rescoring mode that rescores hypotheses232yRfrom the RNN-T decoder230, the deliberation model240is also capable of operating in different modes, such as a beam search mode, depending on design or other factors (e.g., utterance length).

As shown inFIG.2, the first pass206includes the first encoder210and the second encoder220arranged in cascade, which refers to a model structure where the encoding pathway includes two encoders210,220that cascade such that the output of one encoder210feeds the input of the other encoder220prior to decoding. Here, the encoders210,220can be cascaded irrespective of the underlying architecture for each encoder. In some examples, the encoders210,220include a stack of 512-dimension conformer layers. Causal convolution and left-context attention layers may be used for each conformer layer to strictly restrict the model to use no future inputs. A multi-headed (e.g., 8 heads) attention mechanism may be used in a self-attention layer. The cascaded encoders,210,220may include 21 conformer layers. Here, the first encoder210may include 17 conformer layers while the second encoder220may include four conformer layers that take in additional right context (e.g., 0.9 seconds). Optionally, other types of layers incorporating self-attention mechanisms, such as transformer layers, may be used in lieu of conformer layers. The first encoder210may be referred to as a causal encoder and the second encoder220may be referred to as a non-causal encoder.

In other implementations, one encoder is constructed with an LSTM structure while the other encoder is constructed using bi-directional LSTM layers or conformer layers (e.g., a conformer-transducer). In other words, the encoders210,220may have different architectures or similar architectures. For instance, the cascading encoders210,220may be roughly analogous to an acoustic model (AM) in a traditional ASR system, and may include a recurrent network of stacked Long Short-Term Memory (LSTM) layers. Here, the first encoder210is a streaming encoder that includes unidirectional Long Short Term Memory (LSTM) layers while the second encoder220is a non-streaming encoder that includes bidirectional LSTM layers or conformer layers. In a cascading encoder, where both encoders210,220include LSTM layers, the second encoder220that receives the output of the first encoder210may take advantage of the LSTM layers of the first encoder210such that the second encoder220includes fewer LSTM layers than the first encoder210(and fewer LSTM layers than a fully non-streaming model). By having fewer LSTM layers, the cascading encoders may reduce the number of more computationally expensive bidirectional layers making the speech recognizer200more streamlined than simply combining a traditional streaming model with a traditional non-streaming model.

The first encoder210reads a sequence of d-dimensional feature vectors (e.g., acoustic frames) x=x1, x2, . . . , xT), where xt∈d, and produces, at each time step, a first higher-order feature representation as an output212. This first higher-order feature representation may include causal acoustic embeddings and is denoted as es. Similarly, the second encoder220is connected in cascade to the first encoder210, and is trained to receive the first higher order feature esas input, and produce a second higher order feature representation as an output222. This second higher order feature representation includes non-causal acoustic embeddings and is denoted as ea. Both the first encoder210and the second encoder220are directly connected to, and shared by the RNN-T decoder230. Accordingly, the RNN-T decoder230receives both the first higher order feature representation esand the second higher order feature representation eaas inputs. The RNN-T decoder230then decodes the first higher order feature representation esand the second higher order feature representation eainto a first hypothesis speech recognition result yr222. In some examples, each parameterized acoustic frame includes 128-dimensional log-mel features computed within a short shifting window (e.g., 32 milliseconds and shifted every 10 milliseconds). Each feature may be stacked with previous frames (e.g., three previous frames) to form a higher-dimensional vector (e.g., a 512-dimensional vector using the three previous frames). The features forming the vector may then be down-sampled (e.g., to a 30 millisecond frame rate).

In some implementations, the RNN-T decoder230includes a joint layer and an embedding prediction network. Here, the prediction network may have two LSTM layers of 2,048 hidden units and a 640-dimensional projection per layer as well as an embedding layer of 128 units. The RNN-T decoder230uses the joint layer to combine the first and second higher order feature representations es, ea, output by the encoders210,220, as well as an embedding output from the prediction network for the previous prediction yr-1), in order to produce a first pass hypothesis yroutput232. The decoder output232can be a probability distribution, P (yi|yi-1. . . , y0, x), over the current sub-word unit, yi, given the sequence of the N previous non-blank symbols previous units, {yi-1, . . . , yi-N}, and input, x. In some examples, the joint network of the RNN-T decoder230includes 640 hidden units followed by a Softmax layer that predicts 4,096 mixed-case word pieces. In some implementations, the Softmax layer is separate from the RNN-T decoder230and processes the output232, yr, from the RNN-T decoder230. The output of the Softmax layer is then used in a beam search process to select orthographic elements. In some implementations, the Softmax layer is integrated with the RNN-T decoder230, such that the output232, yrof the RNN-T decoder230represents the output of the Softmax layer.

With continued reference toFIG.2, the second pass208uses a deliberation model240that includes a text encoder242and two attention mechanisms244,246, a hypothesis attention mechanism244and an acoustic attention mechanism246, in addition to a deliberation decoder250(also referred to as an LAS decoder250). As described in greater detail below (e.g.,FIGS.3A-3C), the text encoder242of the deliberation model240may be pre-trained using text-only (e.g., unspoken textual utterances) inputs. Here, the speech recognizer200attends to both acoustics, by attending to the second higher order feature representation eaoutput222of the second encoder220at the acoustic attention mechanism246, and the first-pass hypotheses yr, by attending to the outputs232of the RNN-T decoder230at the hypothesis attention mechanism244. By attending to both acoustics (e.g., the output222represented as ea) and the first-pass hypotheses (e.g., the output232represented as yr), the deliberation model240generates the second pass hypothesis as output248(e.g., a prediction sequence). Here, each attention mechanism244,246forms a context vector245,247(e.g., a hypothesis context vector245and an acoustic context vector247, or a first context vector245and a second context vector247) that is input into the deliberation decoder250of the deliberation model240. These context vectors245,247may be concatenated as inputs into the deliberation decoder250.

The text encoder242further encodes the output232of the RNN-T decoder230(i.e., the output232of the first pass206) to form the encoded hypotheses243(e.g., shown as hB). When further encoding the output232, the text encoder242may also encode the output232for useful context information to include in the encoded hypotheses243. For example, the text encoder242is a bidirectional encoder capable of including the context information. The text encoder242may also be configured to encode multiple first hypotheses (i.e., output232). For instance, the text encoder242encodes each hypothesis232separately and then concatenates each encoded hypothesis together. The text encoder242may include a stack of multi-head attention blocks400(referred to herein as conformer blocks400) which may include conformers or transformers. Each multi-head attention block400may include a multi-head attention mechanism420(FIG.4). For example, the text encoder242may be a two-layer conformer encoder, where each layer has a 640-dimensional projection per layer with a multi-token (e.g., two token) right context.

During the second pass208, the speech recognizer200may perform a beam search mode or a rescoring mode to generate the output248(i.e., the second pass hypothesis). In a rescoring mode, the deliberation model240may run on the output232in a teacher-forcing mode. Additionally or alternatively, when in a rescoring mode, using a bidirectional text encoder242may help to improve the relative WER of the deliberation decoder two-pass architecture of the deliberation model240. When the deliberation decoder250operates in a beam search mode, the deliberation decoder250produces the second pass hypothesis as the output248from the output222alone; ignoring the output232of the RNN-T decoder230. When the deliberation decoder250operates in the rescoring mode, the deliberation decoder250obtains the top-K hypotheses (e.g.,4first-pass hypotheses) from the RNN-T decoder230and then the deliberation decoder250is run on each sequence in a teacher-forcing mode, with attention on the output222, to compute a score. For example, a score combines a log probability of the sequence and an attention coverage penalty. The deliberation decoder250selects a sequence with the highest score to be the output222. Here, the deliberation decoder250may include multi-headed attention (e.g., with four heads) to attend to the output222. Furthermore, the deliberation decoder250may be a two-layer LSTM network followed by a softmax layer for prediction. For instance, each layer of the deliberation decoder250has 2,048 hidden units followed by a 640-dimensional projection. The softmax layer may include 4,096 dimensions to predict the same mixed-case word pieces from the softmax layer of the RNN-T decoder230. Much like the attention mechanism inherent to the deliberation decoder250as described above, the attention mechanisms244,246may have a similar structure such that each attention mechanism244,246includes multi-headed attention (e.g., eight heads).

A neural network is generally trained by back propagation that defines a loss function (e.g., a cross-entropy loss function). For instance, the loss function is defined as a difference between the actual outputs of the network and the desired outputs of the network. Here, the speech recognizer200may be trained using a cross entropy loss approach, a joint training approach, or a combination of cross entropy loss and joint training. In a cross entropy loss approach, a deliberation model, such as the speech recognizer200with the deliberation model240(i.e., deliberation-based speech recognizer200), is trained in a two-step training process. During the first step of the training process, the RNN-T decoder230is trained. After the RNN-T decoder220has been trained, parameters for the RNN-T decoder230are fixed and only the deliberation model240and additional encoder layers (e.g., the text encoder242) are trained.

FIGS.3A-3Cshow example training processes300a-300cfor training the deliberation model240of the speech recognizer200. In some configurations, the training processes300a-300cexecute on the remote computing device60ofFIGS.1A and1B. The training processes300a-cobtain set of training data320,320a-nstored in a sample database310and trains the deliberation model240on the training data320. The training data320includes a plurality of training unspoken textual utterances330,330a-n. Here, each training unspoken textual utterance330is not paired with any corresponding spoken utterance of non-synthetic speech. The sample database310may reside on the memory hardware of the remote computing device60. In the examples shown, the training data320is chosen to train the deliberation model240that includes the text encoder242and the deliberation decoder250. Here, the deliberation model240receives the training data320and generates an output which is tested for its accuracy. WhileFIGS.3A-3Cdepict separate training processes300a-c, it should be appreciated that deliberation decoder250may be trained by any combination of the training processes300a-c.

Referring toFIG.3A, a training process300atrains the deliberation model240by pre-training the text encoder242on each training unspoken textual utterance330in the training data320. Here, the training process300aincludes a masking module340that processes each training unspoken textual utterance330before training the text encoder242using cross entropy loss. The masking module340includes a token module342and a masker346. Intuitively, because the text encoder242is bi-directional (i.e., it has both right and left context), it can easily predict target words within a training sample. In order to train the text encoder242, a percentage of each of the training unspoken textual utterance330are masked at random. The token module342obtains each corresponding training unspoken textual utterance330and tokenizes the corresponding training unspoken textual utterance330into a tokenized sequence of sub-word units344,344a-n. The masker346receives the tokenized sequence of sub-word units344and randomly chooses a percentage (e.g., 15%) of the tokenized sequence of sub-word units344to replace with a differing token. For example, the masker346replaces each tokenized sub-word unit344in a first portion of the tokenized sequence of sub-words units344with a mask token344M, and replaces each tokenized sub-word unit344in a second portion of the tokenized sequence of sub-word units344with a random token344M. In particular, the masker346replaces each tokenized sub-word unit344in the tokenized sequence of sub-word units344with a mask token344M 80% of the time, with a random token344R 10% of the time, and leaves the tokenized sub-word unit344unchanged 10% of the time. For example, as shown inFIG.3A, the masking module340obtains the training unspoken textual utterance330aand tokenizes (i.e., using the token module340) the training unspoken textual utterance330ainto the sequence of sub-word units344to produce four tokenized sub-word units344. The masker346receives the four tokenized sub-word units344, and outputs two tokenized sub-word units344that are unchanged, one mask token344M, and one random token344R. Once the training process300ais complete, the parameters of the text encoder242and the deliberation decoder250are updated jointly in additional training, while the parameters for the RNN-T decoder230remain fixed.

Referring toFIG.3B, a training process300btrains the deliberation model240using the training unspoken textual utterances330in the training data320. Here, the training process300bincludes a text-to-speech module350that processes each training unspoken textual utterance330before training the deliberation model340. In particular, the text-to-speech module350obtains each training unspoken textual utterance330and generates, using a text-to-speech model, a corresponding synthetic speech representation352. The training process300bthen trains the deliberation model240using the training unspoken textual utterance330and the corresponding synthetic speech representation352. For example, as shown inFIG.3B, the text-to-speech module350receives the training unspoken textual utterance330aand generates, using the text-to-speech model, the corresponding synthetic speech representation352as output. The training process300bthen jointly trains the deliberation model240using the training unspoken textual utterance330aand the corresponding synthetic speech representation352. The training process300bmay compute both audio and text attention when using the training unspoken textual utterance330aand fixed context vectors to replace the attention when the corresponding synthetic speech representation352is used. The training process300bmay select a mix (e.g., a 1:9 ratio) of the training unspoken textual utterances330and the corresponding synthetic speech representation352when training the deliberation model240.

Referring toFIG.3C, a training process300ctrains the deliberation model240using the training data320. Here, training data320further includes a plurality of training un-transcribed non-synthetic speech utterances332,332a-n. Here, each training un-transcribed non-synthetic speech utterance332is not paired with a corresponding transcription. The training process300cmay use a trained speech recognition model360(also referred to as an ASR model360) that is trained to predict, based on an input utterance, a corresponding transcription of the input utterance as output. In particular, the ASR model360obtains each training un-transcribed non-synthetic speech utterance332and generates a corresponding transcription362. The training process300cthen trains the deliberation model240by training the deliberation decoder250using the training un-transcribed non-synthetic speech utterance332and the corresponding transcription362. For example, as shown inFIG.3C, the ASR model360receives the training un-transcribed non-synthetic speech utterance332aand generates, using the ASR model360, the corresponding transcription362as output. The training process300cthen trains the deliberation decoder250using the training un-transcribed non-synthetic speech utterances332and the corresponding predicted transcriptions362as semi-supervised data.

FIG.4provides an example of a Conformer block400from the stack of Conformer layers of the text encoder242. The Conformer block400includes a first half feed-forward layer410, a second half feed-forward layer440, with a multi-head self-attention block420and a convolution layer430disposed between the first and second half feed-forward layers410,440, and concatenation operators405. The first half feed-forward layer410processes the input hypotheses (e.g., the output232from the RNN-T decoder230). Subsequently, the multi-head self-attention block420receives the input hypotheses concatenated with the output of the first half-feed forward layer410. Intuitively, the role of the multi-head self-attention block420is to summarize context separately for each input frame that is to be enhanced. A convolution layer430subsamples the output of the multi-head self-attention block420concatenated with the output of the first half feed forward layer410. Thereafter, a second half-feed forward layer440receives a concatenation of the convolution layer430output and the multi-head self-attention block420. A layernorm module450processes the output from the second half feed-forward layer440. Mathematically, the conformer block400transforms input features x, using modulation features m, to produce output features y, as follows:

FIG.5is a flowchart of an example arrangement of operations for a method500of performing automated speech recognition (e.g., ASR) using a deliberation two-pass architecture. At operation502, the method500receives training data320including unspoken textual utterances330. Here, each unspoken textual utterance330is not paired with any corresponding spoke utterance of non-synthetic speech. At operation504, the method500includes training a deliberation model240on the unspoken textual utterances330. The deliberation model240includes a text encoder242and a deliberation decoder250.

At operation506, the method500includes receiving, at the trained deliberation model240, first-pass hypotheses232and non-causal acoustic embeddings222. The first-pass hypotheses232are generated by a recurrent neural network-transducer (RNN-T) decoder230for the non-causal acoustic embeddings222encoded by a non-causal encoder220. The method500also includes, at operation508, encoding, using the text encoder242, the first-pass hypotheses232generated by the RNN-T decoder230. At operation510, the method500further includes generating, using the deliberation decoder250attending to both the first-pass hypotheses232and the non-casual acoustic embeddings222, second-pass hypotheses248.

The computing device600includes a processor610, memory620, a storage device630, a high-speed interface/controller640connecting to the memory620and high-speed expansion ports650, and a low speed interface/controller660connecting to a low speed bus670and a storage device630. Each of the components610,620,630,640,650, and660, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor610(e.g., data processing hardware112, or data processing hardware of remote computing device60ofFIG.1) can process instructions for execution within the computing device600, including instructions stored in the memory620or on the storage device630to display graphical information for a graphical user interface (GUI) on an external input/output device, such as display680coupled to high speed interface640. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices600may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

The storage device630is capable of providing mass storage for the computing device600. In some implementations, the storage device630is a computer-readable medium. In various different implementations, the storage device630may be a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In additional implementations, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described above. The information carrier is a computer- or machine-readable medium, such as the memory620, the storage device630, or memory on processor610.

The high speed controller640manages bandwidth-intensive operations for the computing device600, while the low speed controller660manages lower bandwidth-intensive operations. Such allocation of duties is exemplary only. In some implementations, the high-speed controller640is coupled to the memory620, the display680(e.g., through a graphics processor or accelerator), and to the high-speed expansion ports650, which may accept various expansion cards (not shown). In some implementations, the low-speed controller660is coupled to the storage device630and a low-speed expansion port690. The low-speed expansion port690, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, or a networking device such as a switch or router, e.g., through a network adapter.

The computing device600may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server600aor multiple times in a group of such servers600a, as a laptop computer600b, or as part of a rack server system600c.