Patents by Inventor Golan Pundak
Golan Pundak 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: 11942076Abstract: A method includes receiving audio data encoding an utterance spoken by a native speaker of a first language, and receiving a biasing term list including one or more terms in a second language different than the first language. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data to generate speech recognition scores for both wordpieces and corresponding phoneme sequences in the first language. The method also includes rescoring the speech recognition scores for the phoneme sequences based on the one or more terms in the biasing term list, and executing, using the speech recognition scores for the wordpieces and the rescored speech recognition scores for the phoneme sequences, a decoding graph to generate a transcription for the utterance.Type: GrantFiled: February 16, 2022Date of Patent: March 26, 2024Assignee: Google LLCInventors: Ke Hu, Golan Pundak, Rohit Prakash Prabhavalkar, Antoine Jean Bruguier, Tara N. Sainath
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Publication number: 20230419964Abstract: Implementations are directed to causing a voice bot to utilize a plurality of ML layers in resolving unique personal identifier(s) for a human while the voice bot is engaged in a corresponding conversation with the human. The unique personal identifier(s) can include a unique sequence of alphanumeric characters that is personal to the human. In some implementations, ASR speech hypothes(es) corresponding to spoken utterance(s) that include the unique personal identifier(s) can be processed to generate candidate unique personal identifier(s), given alphanumeric character(s) of the candidate unique personal identifier(s) can be selected, and the voice bot can prompt the human with clarification request(s) to clarify the given alphanumeric character(s) until it is predicted to correspond to the an actual unique personal identifier(s) for the human(s). The unique personal identifier(s) can then be utilized in performance of further action(s) by the voice bot and/or other systems.Type: ApplicationFiled: September 7, 2023Publication date: December 28, 2023Inventors: Rafael Goldfarb, Or Guz, Lior Alon, Assaf Hurwitz Michaely, Golan Pundak, Shmuel Leibtag, Tomer Amiaz, Dan Rasin, Asaf Aharoni
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Publication number: 20230377564Abstract: A method for training a speech recognition model with a minimum word error rate loss function includes receiving a training example comprising a proper noun and generating a plurality of hypotheses corresponding to the training example. Each hypothesis of the plurality of hypotheses represents the proper noun and includes a corresponding probability that indicates a likelihood that the hypothesis represents the proper noun. The method also includes determining that the corresponding probability associated with one of the plurality of hypotheses satisfies a penalty criteria. The penalty criteria indicating that the corresponding probability satisfies a probability threshold, and the associated hypothesis incorrectly represents the proper noun. The method also includes applying a penalty to the minimum word error rate loss function.Type: ApplicationFiled: July 31, 2023Publication date: November 23, 2023Applicant: Google LLCInventors: Charles Caleb Peyser, Tara N. Sainath, Golan Pundak
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Patent number: 11790906Abstract: Implementations are directed to causing a voice bot to utilize a plurality of ML layers in resolving unique personal identifier(s) for a human while the voice bot is engaged in a corresponding conversation with the human. The unique personal identifier(s) can include a unique sequence of alphanumeric characters that is personal to the human. In some implementations, ASR speech hypothes(es) corresponding to spoken utterance(s) that include the unique personal identifier(s) can be processed to generate candidate unique personal identifier(s), given alphanumeric character(s) of the candidate unique personal identifier(s) can be selected, and the voice bot can prompt the human with clarification request(s) to clarify the given alphanumeric character(s) until it is predicted to correspond to the an actual unique personal identifier(s) for the human(s). The unique personal identifier(s) can then be utilized in performance of further action(s) by the voice bot and/or other systems.Type: GrantFiled: January 25, 2021Date of Patent: October 17, 2023Assignee: GOOGLE LLCInventors: Rafael Goldfarb, Or Guz, Lior Alon, Assaf Hurwitz Michaely, Golan Pundak, Shmuel Leibtag, Tomer Amiaz, Dan Rasin, Asaf Aharoni
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Patent number: 11749259Abstract: A method for training a speech recognition model with a minimum word error rate loss function includes receiving a training example comprising a proper noun and generating a plurality of hypotheses corresponding to the training example. Each hypothesis of the plurality of hypotheses represents the proper noun and includes a corresponding probability that indicates a likelihood that the hypothesis represents the proper noun. The method also includes determining that the corresponding probability associated with one of the plurality of hypotheses satisfies a penalty criteria. The penalty criteria indicating that the corresponding probability satisfies a probability threshold, and the associated hypothesis incorrectly represents the proper noun. The method also includes applying a penalty to the minimum word error rate loss function.Type: GrantFiled: January 15, 2021Date of Patent: September 5, 2023Assignee: Google LLCInventors: Charles Caleb Peyser, Tara N. Sainath, Golan Pundak
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Publication number: 20230274736Abstract: A method of biasing speech recognition includes receiving audio data encoding an utterance and obtaining a set of one or more biasing phrases corresponding to a context of the utterance. Each biasing phrase in the set of one or more biasing phrases includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data and grapheme and phoneme data derived from the set of one or more biasing phrases to generate an output of the speech recognition model. The method also includes determining a transcription for the utterance based on the output of the speech recognition model.Type: ApplicationFiled: May 4, 2023Publication date: August 31, 2023Applicant: Google LLCInventors: Rohit Prakash Prabhavalkar, Golan Pundak, Tara N. Sainath, Antoine Jean Bruguier
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Patent number: 11664021Abstract: A method of biasing speech recognition includes receiving audio data encoding an utterance and obtaining a set of one or more biasing phrases corresponding to a context of the utterance. Each biasing phrase in the set of one or more biasing phrases includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data and grapheme and phoneme data derived from the set of one or more biasing phrases to generate an output of the speech recognition model. The method also includes determining a transcription for the utterance based on the output of the speech recognition model.Type: GrantFiled: December 9, 2021Date of Patent: May 30, 2023Assignee: Google LLCInventors: Rohit Prakash Prabhavalkar, Golan Pundak, Tara N. Sainath, Antoine Jean Bruguier
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Publication number: 20220392489Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying that a first audio stream includes first, second, and third sources of audio. A computing system identifies that a second audio stream includes the first, second, and third sources of audio. The computing system determines that the first and second sources of audio are part of a first conversation. The computing system generates a third audio stream that combines the first source of audio from the first audio stream, the first source of audio from the second audio stream, the second source of audio from the first audio stream, and the second source of audio from the second audio stream, and diminishes the third source of audio from the first audio stream, and the third source of audio from the second audio stream.Type: ApplicationFiled: August 19, 2022Publication date: December 8, 2022Inventors: Dimitri Kanevsky, Golan Pundak
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Publication number: 20220366897Abstract: A method includes receiving audio data encoding an utterance and obtaining a set of bias phrases corresponding to a context of the utterance. Each bias phrase includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio to generate an output from the speech recognition model. The speech recognition model includes a first encoder configured to receive the acoustic features, a bias encoder configured to receive data indicating the obtained set of bias phrases, a bias encoder, and a decoder configured to determine likelihoods of sequences of speech elements based on output of the first attention module and output of the bias attention module. The method also includes determining a transcript for the utterance based on the likelihoods of sequences of speech elements.Type: ApplicationFiled: July 26, 2022Publication date: November 17, 2022Applicant: Google LLCInventors: Rohit Prakash Prabhavalkar, Golan Pundak, Tara N. Sainath
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Patent number: 11443769Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying that a first audio stream includes first, second, and third sources of audio. A computing system identifies that a second audio stream includes the first, second, and third sources of audio. The computing system determines that the first and second sources of audio are part of a first conversation. The computing system generates a third audio stream that combines the first source of audio from the first audio stream, the first source of audio from the second audio stream, the second source of audio from the first audio stream, and the second source of audio from the second audio stream, and diminishes the third source of audio from the first audio stream, and the third source of audio from the second audio stream.Type: GrantFiled: March 8, 2021Date of Patent: September 13, 2022Assignee: Google LLCInventors: Dimitri Kanevsky, Golan Pundak
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Patent number: 11423883Abstract: A method includes receiving audio data encoding an utterance and obtaining a set of bias phrases corresponding to a context of the utterance. Each bias phrase includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio to generate an output from the speech recognition model. The speech recognition model includes a first encoder configured to receive the acoustic features, a first attention module, a bias encoder configured to receive data indicating the obtained set of bias phrases, a bias encoder, and a decoder configured to determine likelihoods of sequences of speech elements based on output of the first attention module and output of the bias attention module. The method also includes determining a transcript for the utterance based on the likelihoods of sequences of speech elements.Type: GrantFiled: March 31, 2020Date of Patent: August 23, 2022Assignee: Google LLCInventors: Rohit Prakash Prabhavalkar, Golan Pundak, Tara N. Sainath
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Publication number: 20220238105Abstract: Implementations are directed to causing a voice bot to utilize a plurality of ML layers in resolving unique personal identifier(s) for a human while the voice bot is engaged in a corresponding conversation with the human. The unique personal identifier(s) can include a unique sequence of alphanumeric characters that is personal to the human. In some implementations, ASR speech hypothes(es) corresponding to spoken utterance(s) that include the unique personal identifier(s) can be processed to generate candidate unique personal identifier(s), given alphanumeric character(s) of the candidate unique personal identifier(s) can be selected, and the voice bot can prompt the human with clarification request(s) to clarify the given alphanumeric character(s) until it is predicted to correspond to the an actual unique personal identifier(s) for the human(s). The unique personal identifier(s) can then be utilized in performance of further action(s) by the voice bot and/or other systems.Type: ApplicationFiled: January 25, 2021Publication date: July 28, 2022Inventors: Rafael Goldfarb, Or Guz, Lior Alon, Assaf Hurwitz Michaely, Golan Pundak, Shmuel Leibtag, Tomer Amiaz, Dan Rasin, Asaf Aharoni
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Publication number: 20220172706Abstract: A method includes receiving audio data encoding an utterance spoken by a native speaker of a first language, and receiving a biasing term list including one or more terms in a second language different than the first language. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data to generate speech recognition scores for both wordpieces and corresponding phoneme sequences in the first language. The method also includes rescoring the speech recognition scores for the phoneme sequences based on the one or more terms in the biasing term list, and executing, using the speech recognition scores for the wordpieces and the rescored speech recognition scores for the phoneme sequences, a decoding graph to generate a transcription for the utterance.Type: ApplicationFiled: February 16, 2022Publication date: June 2, 2022Applicant: Google LLCInventors: Ke Hu, Golan Pundak, Rohit Prakash Prabhavalkar, Antoine Jean Bruguier, Tara N. Sainath
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Publication number: 20220101836Abstract: A method of biasing speech recognition includes receiving audio data encoding an utterance and obtaining a set of one or more biasing phrases corresponding to a context of the utterance. Each biasing phrase in the set of one or more biasing phrases includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data and grapheme and phoneme data derived from the set of one or more biasing phrases to generate an output of the speech recognition model. The method also includes determining a transcription for the utterance based on the output of the speech recognition model.Type: ApplicationFiled: December 9, 2021Publication date: March 31, 2022Applicant: Google LLCInventors: Rohit Prakash Prabhavalkar, Golan Pundak, Tara N. Sainath, Antoine Jean Bruguier
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Patent number: 11270687Abstract: A method includes receiving audio data encoding an utterance spoken by a native speaker of a first language, and receiving a biasing term list including one or more terms in a second language different than the first language. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data to generate speech recognition scores for both wordpieces and corresponding phoneme sequences in the first language. The method also includes rescoring the speech recognition scores for the phoneme sequences based on the one or more terms in the biasing term list, and executing, using the speech recognition scores for the wordpieces and the rescored speech recognition scores for the phoneme sequences, a decoding graph to generate a transcription for the utterance.Type: GrantFiled: April 28, 2020Date of Patent: March 8, 2022Assignee: Google LLCInventors: Ke Hu, Antoine Jean Bruguier, Tara N. Sainath, Rohit Prakash Prabhavalkar, Golan Pundak
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Patent number: 11217231Abstract: A method of biasing speech recognition includes receiving audio data encoding an utterance and obtaining a set of one or more biasing phrases corresponding to a context of the utterance. Each biasing phrase in the set of one or more biasing phrases includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data and grapheme and phoneme data derived from the set of one or more biasing phrases to generate an output of the speech recognition model. The method also includes determining a transcription for the utterance based on the output of the speech recognition model.Type: GrantFiled: April 30, 2020Date of Patent: January 4, 2022Assignee: Google LLCInventors: Rohit Prakash Prabhavalkar, Golan Pundak, Tara N. Sainath, Antoine Jean Bruguier
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Publication number: 20210233512Abstract: A method for training a speech recognition model with a minimum word error rate loss function includes receiving a training example comprising a proper noun and generating a plurality of hypotheses corresponding to the training example. Each hypothesis of the plurality of hypotheses represents the proper noun and includes a corresponding probability that indicates a likelihood that the hypothesis represents the proper noun. The method also includes determining that the corresponding probability associated with one of the plurality of hypotheses satisfies a penalty criteria. The penalty criteria indicating that the corresponding probability satisfies a probability threshold, and the associated hypothesis incorrectly represents the proper noun. The method also includes applying a penalty to the minimum word error rate loss function.Type: ApplicationFiled: January 15, 2021Publication date: July 29, 2021Applicant: Google LLCInventors: Charles Caleb Peyser, Tara N. Sainath, Golan Pundak
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Publication number: 20210193180Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying that a first audio stream includes first, second, and third sources of audio. A computing system identifies that a second audio stream includes the first, second, and third sources of audio. The computing system determines that the first and second sources of audio are part of a first conversation. The computing system generates a third audio stream that combines the first source of audio from the first audio stream, the first source of audio from the second audio stream, the second source of audio from the first audio stream, and the second source of audio from the second audio stream, and diminishes the third source of audio from the first audio stream, and the third source of audio from the second audio stream.Type: ApplicationFiled: March 8, 2021Publication date: June 24, 2021Inventors: Dimitri Kanevsky, Golan Pundak
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Patent number: 10943619Abstract: In general, the subject matter described in this disclosure can be embodied in methods, systems, and program products for identifying that a first audio stream includes first, second, and third sources of audio. A computing system identifies that a second audio stream includes the first, second, and third sources of audio. The computing system determines that the first and second sources of audio are part of a first conversation. The computing system generates a third audio stream that combines the first source of audio from the first audio stream, the first source of audio from the second audio stream, the second source of audio from the first audio stream, and the second source of audio from the second audio stream, and diminishes the third source of audio from the first audio stream, and the third source of audio from the second audio stream.Type: GrantFiled: March 9, 2020Date of Patent: March 9, 2021Assignee: Google LLCInventors: Dimitri Kanevsky, Golan Pundak
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Publication number: 20200402501Abstract: A method of biasing speech recognition includes receiving audio data encoding an utterance and obtaining a set of one or more biasing phrases corresponding to a context of the utterance. Each biasing phrase in the set of one or more biasing phrases includes one or more words. The method also includes processing, using a speech recognition model, acoustic features derived from the audio data and grapheme and phoneme data derived from the set of one or more biasing phrases to generate an output of the speech recognition model. The method also includes determining a transcription for the utterance based on the output of the speech recognition model.Type: ApplicationFiled: April 30, 2020Publication date: December 24, 2020Applicant: Google LLCInventors: Rohit Prakash Prabhavalkar, Golan Pundak, Tara N. Sainath, Antoine Jean Bruguier