Invention Grant
- Patent Title: Encoder-decoder models for sequence to sequence mapping
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Application No.: US15846634Application Date: 2017-12-19
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Publication No.: US10706840B2Publication Date: 2020-07-07
- Inventor: Hasim Sak , Sean Matthew Shannon
- 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; Grant Griffith
- Main IPC: G10L15/14
- IPC: G10L15/14 ; G10L15/02 ; G10L15/183 ; G10L15/06 ; G10L15/16 ; G06N3/02 ; G06N3/08 ; G10L15/22 ; G06N3/04 ; G06N7/00

Abstract:
Methods, systems, and apparatus for performing speech recognition. In some implementations, acoustic data representing an utterance is obtained. The acoustic data corresponds to time steps in a series of time steps. One or more computers process scores indicative of the acoustic data using a recurrent neural network to generate a sequence of outputs. The sequence of outputs indicates a likely output label from among a predetermined set of output labels. The predetermined set of output labels includes output labels that respectively correspond to different linguistic units and to a placeholder label that does not represent a classification of acoustic data. The recurrent neural network is configured to use an output label indicated for a previous time step to determine an output label for the current time step. The generated sequence of outputs is processed to generate a transcription of the utterance, and the transcription of the utterance is provided.
Public/Granted literature
- US20190057683A1 ENCODER-DECODER MODELS FOR SEQUENCE TO SEQUENCE MAPPING Public/Granted day:2019-02-21
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