Patents by Inventor Yanzhang He
Yanzhang He has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12033641Abstract: Techniques disclosed herein are directed towards streaming keyphrase detection which can be customized to detect one or more particular keyphrases, without requiring retraining of any model(s) for those particular keyphrase(s). Many implementations include processing audio data using a speaker separation model to generate separated audio data which isolates an utterance spoken by a human speaker from one or more additional sounds not spoken by the human speaker, and processing the separated audio data using a text independent speaker identification model to determine whether a verified and/or registered user spoke a spoken utterance captured in the audio data. Various implementations include processing the audio data and/or the separated audio data using an automatic speech recognition model to generate a text representation of the utterance.Type: GrantFiled: January 30, 2023Date of Patent: July 9, 2024Assignee: GOOGLE LLCInventors: Rajeev Rikhye, Quan Wang, Yanzhang He, Qiao Liang, Ian C. McGraw
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Publication number: 20240221750Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers; generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.Type: ApplicationFiled: March 19, 2024Publication date: July 4, 2024Applicant: Google LLCInventors: Wei Li, Rohit Prakash Prabhavalkar, Kanury Kanishka Rao, Yanzhang He, Ian C. McGraw, Anton Bakhtin
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Publication number: 20240169981Abstract: A unified end-to-end segmenter and two-pass automatic speech recognition (ASR) model includes a first encoder, a first decoder, a second encoder, and a second decoder. The first encoder is configured to receive a sequence of acoustic frames and generate a first higher order feature representation. The first decoder is configured to receive the first higher order feature representation and generate, at each of a plurality of output steps, a first probability distribution and an indication of whether the output step corresponds to an end of speech segment, and emit an end of speech timestamp. The second encoder is configured to receive the first higher order feature representation and the end of speech timestamp, and generate a second higher order feature representation. The second decoder is configured to receive the second higher order feature representation and generate a second probability distribution.Type: ApplicationFiled: November 17, 2023Publication date: May 23, 2024Applicant: Google LLCInventors: Wenqian Ronny Huang, Shuo-yiin Chang, Tara N. Sainath, Yanzhang He
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Publication number: 20240153495Abstract: A method includes receiving a training dataset that includes one or more spoken training utterances for training an automatic speech recognition (ASR) model. Each spoken training utterance in the training dataset paired with a corresponding transcription and a corresponding target sequence of auxiliary tokens. For each spoken training utterance, the method includes generating a speech recognition hypothesis for a corresponding spoken training utterance, determining a speech recognition loss based on the speech recognition hypothesis and the corresponding transcription, generating a predicted auxiliary token for the corresponding spoken training utterance, and determining an auxiliary task loss based on the predicted auxiliary token and the corresponding target sequence of auxiliary tokens. The method also includes the ASR model jointly on the speech recognition loss and the auxiliary task loss determined for each spoken training utterance.Type: ApplicationFiled: October 26, 2023Publication date: May 9, 2024Applicant: Google LLCInventors: Weiran Wang, Ding Zhao, Shaojin Ding, Hao Zhang, Shuo-yiin Chang, David Johannes Rybach, Tara N. Sainath, Yanzhang He, Ian McGraw, Shankar Kumar
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Patent number: 11948570Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers; generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.Type: GrantFiled: March 9, 2022Date of Patent: April 2, 2024Assignee: Google LLCInventors: Wei Li, Rohit Prakash Prabhavalkar, Kanury Kanishka Rao, Yanzhang He, Ian C. Mcgraw, Anton Bakhtin
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Publication number: 20240029719Abstract: A single E2E multitask model includes a speech recognition model and an endpointer model. The speech recognition model includes an audio encoder configured to encode a sequence of audio frames into corresponding higher-order feature representations, and a decoder configured to generate probability distributions over possible speech recognition hypotheses for the sequence of audio frames based on the higher-order feature representations. The endpointer model is configured to operate between a VAD mode and an EOQ detection mode. During the VAD mode, the endpointer model receives input audio frames, and determines, for each input audio frame, whether the input audio frame includes speech. During the EOQ detection mode, the endpointer model receives latent representations for the sequence of audio frames output from the audio encoder, and determines, for each of the latent representation, whether the latent representation includes final silence.Type: ApplicationFiled: June 23, 2023Publication date: January 25, 2024Applicant: Google LLCInventors: Shaan Jagdeep Patrick Bijwadia, Shuo-yiin Chang, Bo Li, Yanzhang He, Tara N. Sainath, Chao Zhang
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Publication number: 20230343328Abstract: An ASR model includes a first encoder configured to receive a sequence of acoustic frames and generate a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The ASR model also includes a second encoder configured to receive the first higher order feature representation generated by the first encoder at each of the plurality of output steps and generate a second higher order feature representation for a corresponding first higher order feature frame. The ASR model also includes a decoder configured to receive the second higher order feature representation generated by the second encoder at each of the plurality of output steps and generate a first probability distribution over possible speech recognition hypothesis. The ASR model also includes a language model configured to receive the first probability distribution over possible speech hypothesis and generate a rescored probability distribution.Type: ApplicationFiled: June 16, 2023Publication date: October 26, 2023Applicant: Google LLCInventors: Tara Sainath, Arun Narayanan, Rami Botros, Yanzhang He, Ehsan Variani, Cyril Allauzen, David Rybach, Ruoming Pang, Trevor Strohman
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Publication number: 20230298563Abstract: 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.Type: ApplicationFiled: March 18, 2023Publication date: September 21, 2023Applicant: Google LLCInventors: Ke Hu, Tara N. Sainath, Yanzhang He, Rohit Prabhavalkar, Sepand Mavandadi, Weiran Wang, Trevor Strohman
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Publication number: 20230298591Abstract: A computer-implemented method includes receiving a sequence of acoustic frames corresponding to an utterance and generating a reference speaker embedding for the utterance. The method also includes receiving a target speaker embedding for a target speaker and generating feature-wise linear modulation (FiLM) parameters including a scaling vector and a shifting vector based on the target speaker embedding. The method also includes generating an affine transformation output that scales and shifts the reference speaker embedding based on the FiLM parameters. The method also includes generating a classification output indicating whether the utterance was spoken by the target speaker based on the affine transformation output.Type: ApplicationFiled: March 17, 2023Publication date: September 21, 2023Applicant: Google LLCInventors: Shaojin Ding, Rajeev Rikhye, Qiao Liang, Yanzhang He, Quan Wang, Arun Narayanan, Tom O'Malley, Ian McGraw
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Publication number: 20230298569Abstract: A method for training a model includes obtaining a plurality of training samples. Each respective training sample of the plurality of training samples includes a respective speech utterance and a respective textual utterance representing a transcription of the respective speech utterance. The method includes training, using quantization aware training with native integer operations, an automatic speech recognition (ASR) model on the plurality of training samples. The method also includes quantizing the trained ASR model to an integer target fixed-bit width. The quantized trained ASR model includes a plurality of weights. Each weight of the plurality of weights includes an integer with the target fixed-bit width. The method includes providing the quantized trained ASR model to a user device.Type: ApplicationFiled: March 20, 2023Publication date: September 21, 2023Applicant: Google LLCInventors: Shaojin Ding, Oleg Rybakov, Phoenix Meadowlark, Shivani Agrawal, Yanzhang He, Lukasz Lew
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Patent number: 11715458Abstract: An ASR model includes a first encoder configured to receive a sequence of acoustic frames and generate a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The ASR model also includes a second encoder configured to receive the first higher order feature representation generated by the first encoder at each of the plurality of output steps and generate a second higher order feature representation for a corresponding first higher order feature frame. The ASR model also includes a decoder configured to receive the second higher order feature representation generated by the second encoder at each of the plurality of output steps and generate a first probability distribution over possible speech recognition hypothesis. The ASR model also includes a language model configured to receive the first probability distribution over possible speech hypothesis and generate a rescored probability distribution.Type: GrantFiled: May 10, 2021Date of Patent: August 1, 2023Assignee: Google LLCInventors: Tara Sainath, Arun Narayanan, Rami Botros, Yanzhang He, Ehsan Variani, Cyril Allauzen, David Rybach, Ruoming Pang, Trevor Strohman
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Publication number: 20230186901Abstract: A method includes receiving a training example for a listen-attend-spell (LAS) decoder of a two-pass streaming neural network model and determining whether the training example corresponds to a supervised audio-text pair or an unpaired text sequence. When the training example corresponds to an unpaired text sequence, the method also includes determining a cross entropy loss based on a log probability associated with a context vector of the training example. The method also includes updating the LAS decoder and the context vector based on the determined cross entropy loss.Type: ApplicationFiled: February 10, 2023Publication date: June 15, 2023Applicant: Google LLCInventors: Tara N. Sainath, Ruoming Pang, Ron Weiss, Yanzhang He, Chung-Cheng Chiu, Trevor Strohman
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Publication number: 20230169984Abstract: Techniques disclosed herein are directed towards streaming keyphrase detection which can be customized to detect one or more particular keyphrases, without requiring retraining of any model(s) for those particular keyphrase(s). Many implementations include processing audio data using a speaker separation model to generate separated audio data which isolates an utterance spoken by a human speaker from one or more additional sounds not spoken by the human speaker, and processing the separated audio data using a text independent speaker identification model to determine whether a verified and/or registered user spoke a spoken utterance captured in the audio data. Various implementations include processing the audio data and/or the separated audio data using an automatic speech recognition model to generate a text representation of the utterance.Type: ApplicationFiled: January 30, 2023Publication date: June 1, 2023Inventors: Rajeev Rikhye, Quan Wang, Yanzhang He, Qiao Liang, Ian C. McGraw
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Publication number: 20230108275Abstract: A method includes receiving a sequence of acoustic frames characterizing one or more utterances as input to a multilingual automated speech recognition (ASR) model. The method also includes generating a higher order feature representation for a corresponding acoustic frame. The method also includes generating a hidden representation based on a sequence of non-blank symbols output by a final softmax layer. The method also includes generating a probability distribution over possible speech recognition hypotheses based on the hidden representation generated by the prediction network at each of the plurality of output steps and the higher order feature representation generated by the encoder at each of the plurality of output steps. The method also includes predicting an end of utterance (EOU) token at an end of each utterance. The method also includes classifying each acoustic frame as either speech, initial silence, intermediate silence, or final silence.Type: ApplicationFiled: September 22, 2022Publication date: April 6, 2023Applicant: Google LLCInventors: Bo Li, Tara N. Sainath, Ruoming Pang, Shuo-yin Chang, Qiumin Xu, Trevor Strohman, Vince Chen, Qiao Liang, Heguang Liu, Yanzhang He, Parisa Haghani, Sameer Bidichandani
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Patent number: 11610586Abstract: A method includes receiving a speech recognition result, and using a confidence estimation module (CEM), for each sub-word unit in a sequence of hypothesized sub-word units for the speech recognition result: obtaining a respective confidence embedding that represents a set of confidence features; generating, using a first attention mechanism, a confidence feature vector; generating, using a second attention mechanism, an acoustic context vector; and generating, as output from an output layer of the CEM, a respective confidence output score for each corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the CEM. For each of the one or more words formed by the sequence of hypothesized sub-word units, the method also includes determining a respective word-level confidence score for the word. The method also includes determining an utterance-level confidence score by aggregating the word-level confidence scores.Type: GrantFiled: February 23, 2021Date of Patent: March 21, 2023Assignee: Google LLCInventors: David Qiu, Qiujia Li, Yanzhang He, Yu Zhang, Bo Li, Liangliang Cao, Rohit Prabhavalkar, Deepti Bhatia, Wei Li, Ke Hu, Tara Sainath, Ian Mcgraw
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Patent number: 11594212Abstract: A method includes receiving a training example for a listen-attend-spell (LAS) decoder of a two-pass streaming neural network model and determining whether the training example corresponds to a supervised audio-text pair or an unpaired text sequence. When the training example corresponds to an unpaired text sequence, the method also includes determining a cross entropy loss based on a log probability associated with a context vector of the training example. The method also includes updating the LAS decoder and the context vector based on the determined cross entropy loss.Type: GrantFiled: January 21, 2021Date of Patent: February 28, 2023Assignee: Google LLCInventors: Tara N. Sainath, Ruoming Pang, Ron Weiss, Yanzhang He, Chung-Cheng Chiu, Trevor Strohman
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Publication number: 20230038982Abstract: A method for automatic speech recognition using joint acoustic echo cancellation, speech enhancement, and voice separation includes receiving, at a contextual frontend processing model, input speech features corresponding to a target utterance. The method also includes receiving, at the contextual frontend processing model, at least one of a reference audio signal, a contextual noise signal including noise prior to the target utterance, or a speaker embedding including voice characteristics of a target speaker that spoke the target utterance. The method further includes processing, using the contextual frontend processing model, the input speech features and the at least one of the reference audio signal, the contextual noise signal, or the speaker embedding vector to generate enhanced speech features.Type: ApplicationFiled: December 14, 2021Publication date: February 9, 2023Applicant: Google LLCInventors: Arun Narayanan, Tom O'malley, Quan Wang, Alex Park, James Walker, Nathan David Howard, Yanzhang He, Chung-Cheng Chiu
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Patent number: 11568878Abstract: Techniques disclosed herein are directed towards streaming keyphrase detection which can be customized to detect one or more particular keyphrases, without requiring retraining of any model(s) for those particular keyphrase(s). Many implementations include processing audio data using a speaker separation model to generate separated audio data which isolates an utterance spoken by a human speaker from one or more additional sounds not spoken by the human speaker, and processing the separated audio data using a text independent speaker identification model to determine whether a verified and/or registered user spoke a spoken utterance captured in the audio data. Various implementations include processing the audio data and/or the separated audio data using an automatic speech recognition model to generate a text representation of the utterance.Type: GrantFiled: April 16, 2021Date of Patent: January 31, 2023Assignee: GOOGLE LLCInventors: Rajeev Rikhye, Quan Wang, Yanzhang He, Qiao Liang, Ian C. McGraw
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Publication number: 20220335953Abstract: Techniques disclosed herein are directed towards streaming keyphrase detection which can be customized to detect one or more particular keyphrases, without requiring retraining of any model(s) for those particular keyphrase(s). Many implementations include processing audio data using a speaker separation model to generate separated audio data which isolates an utterance spoken by a human speaker from one or more additional sounds not spoken by the human speaker, and processing the separated audio data using a text independent speaker identification model to determine whether a verified and/or registered user spoke a spoken utterance captured in the audio data. Various implementations include processing the audio data and/or the separated audio data using an automatic speech recognition model to generate a text representation of the utterance.Type: ApplicationFiled: April 16, 2021Publication date: October 20, 2022Inventors: Rajeev Rikhye, Quan Wang, Yanzhang He, Qiao Liang, Ian C. McGraw
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Publication number: 20220310080Abstract: A method including receiving a speech recognition result corresponding to a transcription of an utterance spoken by a user. For each sub-word unit in a sequence of hypothesized sub-word units of the speech recognition result, using a confidence estimation module to: obtain a respective confidence embedding associated with the corresponding output step when the corresponding sub-word unit was output from the first speech recognizer; generate a confidence feature vector; generate an acoustic context vector; and generate a respective confidence output score for the corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the confidence estimation module. The method also includes determining, based on the respective confidence output score generated for each sub-word unit in the sequence of hypothesized sub-word units, an utterance-level confidence score for the transcription of the utterance.Type: ApplicationFiled: December 11, 2021Publication date: September 29, 2022Applicant: Google LLCInventors: David Qiu, Yanzhang He, Yu Zhang, Qiujia Li, Liangliang Cao, Ian McGraw