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公开(公告)号：US11080589B2
公开(公告)日：2021-08-03
申请号：US16504924
申请日：2019-07-08
Applicant: Google LLC
Inventor： Ron J. Weiss , Thang Minh Luong , Peter J. Liu , Colin Abraham Raffel , Douglas Eck
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence including a respective output at each of multiple output time steps from respective encoded representations of inputs in an input sequence. The method includes, for each output time step, starting from the position, in the input order, of the encoded representation that was selected as a preceding context vector at a preceding output time step, traversing the encoded representations until an encoded representation is selected as a current context vector at the output time step. A decoder neural network processes the current context vector and a preceding output at the preceding output time step to generate a respective output score for each possible output and to update the hidden state of the decoder recurrent neural network. An output is selected for the output time step using the output scores.
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公开(公告)号：US20210217404A1
公开(公告)日：2021-07-15
申请号：US17055951
申请日：2019-05-17
Applicant: Google LLC
Inventor： Ye Jia , Zhifeng Chen , Yonghui Wu , Jonathan Shen , Ruoming Pang , Ron J. Weiss , Ignacio Lopez Moreno , Fei Ren , Yu Zhang , Quan Wang , Patrick Nguyen
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.
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公开(公告)号：US20200160836A1
公开(公告)日：2020-05-21
申请号：US16684483
申请日：2019-11-14
Applicant: GOOGLE LLC
Inventor： Zhifeng Chen , Bo Li , Eugene Weinstein , Yonghui Wu , Pedro J. Moreno Mengibar , Ron J. Weiss , Khe Chai Sim , Tara N. Sainath , Patrick An Phu Nguyen
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable media, for speech recognition using multi-dialect and multilingual models. In some implementations, audio data indicating audio characteristics of an utterance is received. Input features determined based on the audio data are provided to a speech recognition model that has been trained to output score indicating the likelihood of linguistic units for each of multiple different language or dialects. The speech recognition model can be one that has been trained using cluster adaptive training. Output that the speech recognition model generated in response to receiving the input features determined based on the audio data is received. A transcription of the utterance generated based on the output of the speech recognition model is provided.
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公开(公告)号：US20200051583A1
公开(公告)日：2020-02-13
申请号：US16058640
申请日：2018-08-08
Applicant: Google LLC
Inventor： Yonghui Wu , Jonathan Shen , Ruoming Pang , Ron J. Weiss
Abstract: Methods, systems, and computer program products for generating, from an input character sequence, an output sequence of audio data representing the input character sequence. The output sequence of audio data includes a respective audio output sample for each of a number of time steps. One example method includes, for each of the time steps: generating a mel-frequency spectrogram for the time step by processing a representation of a respective portion of the input character sequence using a decoder neural network; generating a probability distribution over a plurality of possible audio output samples for the time step by processing the mel-frequency spectrogram for the time step using a vocoder neural network; and selecting the audio output sample for the time step from the possible audio output samples in accordance with the probability distribution.
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公开(公告)号：US20190259409A1
公开(公告)日：2019-08-22
申请号：US16278830
申请日：2019-02-19
Applicant: Google LLC
Inventor： Ehsan Variani , Kevin William Wilson , Ron J. Weiss , Tara N. Sainath , Arun Narayanan
IPC: G10L25/30 , G10L21/028 , G10L19/008 , G10L15/20 , G10L15/16 , G10L21/0388
Abstract: This specification describes computer-implemented methods and systems. One method includes receiving, by a neural network of a speech recognition system, first data representing a first raw audio signal and second data representing a second raw audio signal. The first raw audio signal and the second raw audio signal describe audio occurring at a same period of time. The method further includes generating, by a spatial filtering layer of the neural network, a spatial filtered output using the first data and the second data, and generating, by a spectral filtering layer of the neural network, a spectral filtered output using the spatial filtered output. Generating the spectral filtered output comprises processing frequency-domain data representing the spatial filtered output. The method still further includes processing, by one or more additional layers of the neural network, the spectral filtered output to predict sub-word units encoded in both the first raw audio signal and the second raw audio signal.
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Patent Agency Ranking
公开(公告)号：US12300216B2
公开(公告)日：2025-05-13
申请号：US17310732
申请日：2019-11-26
Applicant: Google LLC
Inventor： Fadi Biadsy , Ron J. Weiss , Aleksandar Kracun , Pedro J. Moreno Mengibar
IPC: G10L13/02 , G06N3/08 , G10L13/027 , G10L13/08 , G10L15/06 , G10L21/003 , G10L21/04 , G10L21/10 , G10L25/18 , G10L25/30 , H04L51/02
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for end to end speech conversion are disclosed. In one aspect, a method includes the actions of receiving first audio data of a first utterance of one or more first terms spoken by a user. The actions further include providing the first audio data as an input to a model that is configured to receive first given audio data in a first voice and output second given audio data in a synthesized voice without performing speech recognition on the first given audio data. The actions further include receiving second audio data of a second utterance of the one or more first terms spoken in the synthesized voice. The actions further include providing, for output, the second audio data of the second utterance of the one or more first terms spoken in the synthesized voice.
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公开(公告)号：US12190860B2
公开(公告)日：2025-01-07
申请号：US18516069
申请日：2023-11-21
Applicant: Google LLC
Inventor： Samuel Bengio , Yuxuan Wang , Zongheng Yang , Zhifeng Chen , Yonghui Wu , Ioannis Agiomyrgiannakis , Ron J. Weiss , Navdeep Jaitly , Ryan M. Rifkin , Robert Andrew James Clark , Quoc V. Le , Russell J. Ryan , Ying Xiao
IPC: G10L13/06 , G06N3/045 , G06N3/08 , G06N3/084 , G10L13/04 , G10L13/08 , G10L15/16 , G10L25/18 , G10L25/30
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. One of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.
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公开(公告)号：US12148444B2
公开(公告)日：2024-11-19
申请号：US17222736
申请日：2021-04-05
Applicant: Google LLC
Inventor： Yonghui Wu , Jonathan Shen , Ruoming Pang , Ron J. Weiss , Michael Schuster , Navdeep Jaitly , Zongheng Yang , Zhifeng Chen , Yu Zhang , Yuxuan Wang , Russell John Wyatt Skerry-Ryan , Ryan M. Rifkin , Ioannis Agiomyrgiannakis
Abstract: Methods, systems, and computer program products for generating, from an input character sequence, an output sequence of audio data representing the input character sequence. The output sequence of audio data includes a respective audio output sample for each of a number of time steps. One example method includes, for each of the time steps: generating a mel-frequency spectrogram for the time step by processing a representation of a respective portion of the input character sequence using a decoder neural network; generating a probability distribution over a plurality of possible audio output samples for the time step by processing the mel-frequency spectrogram for the time step using a vocoder neural network; and selecting the audio output sample for the time step from the possible audio output samples in accordance with the probability distribution.
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公开(公告)号：US20240161730A1
公开(公告)日：2024-05-16
申请号：US18421116
申请日：2024-01-24
Applicant: Google LLC
Inventor： Isaac Elias , Jonathan Shen , Yu Zhang , Ye Jia , Ron J. Weiss , Yonghui Wu , Byungha Chun
IPC: G10L13/08 , G06F40/126 , G06N3/044 , G06N3/045 , G06N3/08 , G06N3/088 , G10L13/047 , G10L21/10
CPC classification number: G10L13/08 , G06F40/126 , G06N3/044 , G06N3/045 , G06N3/08 , G06N3/088 , G10L13/047 , G10L21/10 , G06N3/048
Abstract: A method for training a non-autoregressive TTS model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. The method also includes encoding the reference audio signal into a variational embedding that disentangles the style/prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. The method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. The method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. The method also includes training the TTS model based on the final spectrogram loss and the corresponding phoneme duration loss.
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公开(公告)号：US20240127791A1
公开(公告)日：2024-04-18
申请号：US18516069
申请日：2023-11-21
Applicant: Google LLC
Inventor： Samuel Bengio , Yuxuan Wang , Zongheng Yang , Zhifeng Chen , Yonghui Wu , Ioannis Agiomyrgiannakis , Ron J. Weiss , Navdeep Jaitly , Ryan M. Rifkin , Robert Andrew James Clark , Quoc V. Le , Russell J. Ryan , Ying Xiao
CPC classification number: G10L13/08 , G06N3/045 , G06N3/08 , G06N3/084 , G10L13/04 , G10L15/16 , G10L25/18 , G10L25/30
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating speech from text. One of the systems includes one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to implement: a sequence-to-sequence recurrent neural network configured to: receive a sequence of characters in a particular natural language, and process the sequence of characters to generate a spectrogram of a verbal utterance of the sequence of characters in the particular natural language; and a subsystem configured to: receive the sequence of characters in the particular natural language, and provide the sequence of characters as input to the sequence-to-sequence recurrent neural network to obtain as output the spectrogram of the verbal utterance of the sequence of characters in the particular natural language.
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