Invention Grant
- Patent Title: Encoder-decoder models for sequence to sequence mapping
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Application No.: US16886278Application Date: 2020-05-28
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Publication No.: US11776531B2Publication Date: 2023-10-03
- 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: Oblon, McClelland, Maier & Neustadt, L.L.P.
- Main IPC: G06N3/02
- IPC: G06N3/02 ; G06N3/044 ; G06N3/045 ; G06N3/084 ; G06N7/01 ; G10L15/02 ; G10L15/06 ; G10L15/14 ; G10L15/16 ; G10L15/22 ; G10L15/183

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.
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