Invention Grant
- Patent Title: Reconciliation between simulator and speech recognition output using sequence-to-sequence mapping
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Application No.: US16436704Application Date: 2019-06-10
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Publication No.: US10559299B1Publication Date: 2020-02-11
- Inventor: Itamar Arel , Joshua Benjamin Looks , Ali Ziaei , Michael Lefkowitz
- Applicant: Apprente LLC
- Applicant Address: US CA Mountain View
- Assignee: Apprente LLC
- Current Assignee: Apprente LLC
- Current Assignee Address: US CA Mountain View
- Agency: Lowenstein Sandler LLP
- Main IPC: G01L15/00
- IPC: G01L15/00 ; G10L15/06 ; G06N20/00 ; G10L15/02

Abstract:
A synthetic training data item comprising a first sequence of symbols that represent a synthetic sentence output by a simulator is received. The synthetic training data item is processed using a machine learning model, which outputs a second sequence of symbols that represent the synthetic sentence. The synthetic training data item is modified by replacing the first sequence of symbols with the second sequence of symbols. A statistically significant mismatch exists between the first sequence of symbols and a third sequence of symbols that would be output by an acoustic model that processes a set of acoustic features that represent an utterance of the synthetic sentence, and no statistically significant mismatch exists between the second sequence of symbols and the third sequence of symbols. The modified synthetic training data item may be used to train a second machine learning model that processes data output by the acoustic model.
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