公开(公告)号：US11158321B2

公开(公告)日：2021-10-26

申请号：US16580726

申请日：2019-09-24

Applicant: Google LLC

Inventor： Asaf Aharoni ,  Arun Narayanan ,  Nir Shabat ,  Parisa Haghani ,  Galen Tsai Chuang ,  Yaniv Leviathan ,  Neeraj Gaur ,  Pedro J. Moreno Mengibar ,  Rohit Prakash Prabhavalkar ,  Zhongdi Qu ,  Austin Severn Waters ,  Tomer Amiaz ,  Michiel A. U. Bacchiani

IPC: G10L15/26 ,  H04M3/428 ,  H04M1/02 ,  G10L15/32 ,  H04M3/51 ,  H04M1/663

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for an automated calling system are disclosed. In one aspect, a method includes the actions of receiving audio data of an utterance spoken by a user who is having a telephone conversation with a bot. The actions further include determining a context of the telephone conversation. The actions further include determining a user intent of a first previous portion of the telephone conversation spoken by the user and a bot intent of a second previous portion of the telephone conversation outputted by a speech synthesizer of the bot. The actions further include, based on the audio data of the utterance, the context of the telephone conversation, the user intent, and the bot intent, generating synthesized speech of a reply by the bot to the utterance. The actions further include, providing, for output, the synthesized speech.

公开(公告)号：US10672384B2

公开(公告)日：2020-06-02

申请号：US16573323

申请日：2019-09-17

Applicant: Google LLC

Inventor： Georg Heigold ,  Erik McDermott ,  Vincent O. Vanhoucke ,  Andrew W. Senior ,  Michiel A. U. Bacchiani

IPC: G10L15/06 ,  G10L15/16 ,  G10L15/183 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.

公开(公告)号：US10332515B2

公开(公告)日：2019-06-25

申请号：US15458214

申请日：2017-03-14

Applicant: Google LLC

Inventor： Chanwoo Kim ,  Rajeev Conrad Nongpiur ,  Michiel A. U. Bacchiani

IPC: G10L15/25 ,  G10L15/22 ,  G10L15/04 ,  G10L25/78 ,  G06K9/00 ,  G10L15/26

Abstract: Systems and methods are described for improving endpoint detection of a voice query submitted by a user. In some implementations, a synchronized video data and audio data is received. A sequence of frames of the video data that includes images corresponding to lip movement on a face is determined. The audio data is endpointed based on first audio data that corresponds to a first frame of the sequence of frames and second audio data that corresponds to a last frame of the sequence of frames. A transcription of the endpointed audio data is generated by an automated speech recognizer. The generated transcription is then provided for output.