Invention Grant
- Patent Title: Applying neural network language models to weighted finite state transducers for automatic speech recognition
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Application No.: US16035513Application Date: 2018-07-13
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Publication No.: US10354652B2Publication Date: 2019-07-16
- Inventor: Rongqing Huang , Ilya Oparin
- Applicant: Apple Inc.
- Applicant Address: US CA Cupertino
- Assignee: Apple Inc.
- Current Assignee: Apple Inc.
- Current Assignee Address: US CA Cupertino
- Agency: Dentons US LLP
- Main IPC: G10L15/28
- IPC: G10L15/28 ; G10L15/14 ; G10L15/16 ; G10L15/19 ; G10L15/197 ; G10L15/193 ; G10L15/08

Abstract:
Systems and processes for converting speech-to-text are provided. In one example process, speech input can be received. A sequence of states and arcs of a weighted finite state transducer (WFST) can be traversed. A negating finite state transducer (FST) can be traversed. A virtual FST can be composed using a neural network language model and based on the sequence of states and arcs of the WFST. The one or more virtual states of the virtual FST can be traversed to determine a probability of a candidate word given one or more history candidate words. Text corresponding to the speech input can be determined based on the probability of the candidate word given the one or more history candidate words. An output can be provided based on the text corresponding to the speech input.
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