Patents by Inventor Tongzhou Chen

Tongzhou Chen 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).

  • Publication number: 20220310074
    Abstract: A method for an automated speech recognition (ASR) model for unifying streaming and non-streaming speech recognition including receiving a sequence of acoustic frames. The method includes generating, using an audio encoder of an automatic speech recognition (ASR) model, a higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method further includes generating, using a joint encoder of the ASR model, a probability distribution over possible speech recognition hypothesis at the corresponding time step based on the higher order feature representation generated by the audio encoder at the corresponding time step. The audio encoder comprises a neural network that applies mixture model (MiMo) attention to compute an attention probability distribution function (PDF) using a set of mixture components of softmaxes over a context window.
    Type: Application
    Filed: December 15, 2021
    Publication date: September 29, 2022
    Applicant: Google LLC
    Inventors: Kartik Audhkhasi, Bhuvana Ramabhadran, Tongzhou Chen, Pedro J. Moreno Mengibar
  • Publication number: 20220310073
    Abstract: A method for an automated speech recognition (ASR) model for unifying streaming and non-streaming speech recognition including receiving a sequence of acoustic frames. The method includes generating, using an audio encoder of an automatic speech recognition (ASR) model, a higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method further includes generating, using a joint encoder of the ASR model, a probability distribution over possible speech recognition hypothesis at the corresponding time step based on the higher order feature representation generated by the audio encoder at the corresponding time step. The audio encoder comprises a neural network that applies mixture model (MiMo) attention to compute an attention probability distribution function (PDF) using a set of mixture components of softmaxes over a context window.
    Type: Application
    Filed: December 15, 2021
    Publication date: September 29, 2022
    Applicant: Google LLC
    Inventors: Kartik Audhkhasi, Bhuvana Ramabhadran, Tongzhou Chen, Pedro J. Moreno Mengibar
  • Publication number: 20220310081
    Abstract: A method includes receiving a sequence of acoustic frames extracted from audio data corresponding to an utterance. During a first pass, the method includes processing the sequence of acoustic frames to generate N candidate hypotheses for the utterance. During a second pass, and for each candidate hypothesis, the method includes generating a respective un-normalized likelihood score; generating a respective external language model score; generating a standalone score that models prior statistics of the corresponding candidate hypothesis, and generating a respective overall score for the candidate hypothesis based on the un-normalized likelihood score, the external language model score, and the standalone score. The method also includes selecting the candidate hypothesis having the highest respective overall score from among the N candidate hypotheses as a final transcription of the utterance.
    Type: Application
    Filed: March 22, 2022
    Publication date: September 29, 2022
    Applicant: Google LLC
    Inventors: Neeraj Gaur, Tongzhou Chen, Ehsan Variani, Bhuvana Ramabhadran, Parisa Haghani, Pedro J. Moreno Mengibar