Invention Grant
- Patent Title: Speech recognition with sequence-to-sequence models
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Application No.: US16516390Application Date: 2019-07-19
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Publication No.: US11145293B2Publication Date: 2021-10-12
- Inventor: Rohit Prakash Prabhavalkar , Zhifeng Chen , Bo Li , Chung-Cheng Chiu , Kanury Kanishka Rao , Yonghui Wu , Ron J. Weiss , Navdeep Jaitly , Michiel A. U. Bacchiani , Tara N. Sainath , Jan Kazimierz Chorowski , Anjuli Patricia Kannan , Ekaterina Gonina , Patrick An Phu Nguyen
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Agency: Honigman LLP
- Agent Brett A. Krueger
- Main IPC: G10L15/00
- IPC: G10L15/00 ; G10L15/16 ; G10L15/22 ; G10L15/02 ; G06N3/08 ; G10L15/06 ; G10L25/30 ; G10L15/26

Abstract:
Methods, systems, and apparatus, including computer-readable media, for performing speech recognition using sequence-to-sequence models. An automated speech recognition (ASR) system receives audio data for an utterance and provides features indicative of acoustic characteristics of the utterance as input to an encoder. The system processes an output of the encoder using an attender to generate a context vector and generates speech recognition scores using the context vector and a decoder trained using a training process that selects at least one input to the decoder with a predetermined probability. An input to the decoder during training is selected between input data based on a known value for an element in a training example, and input data based on an output of the decoder for the element in the training example. A transcription is generated for the utterance using word elements selected based on the speech recognition scores. The transcription is provided as an output of the ASR system.
Public/Granted literature
- US20200027444A1 SPEECH RECOGNITION WITH SEQUENCE-TO-SEQUENCE MODELS Public/Granted day:2020-01-23
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