Invention Grant
US09037463B2 Efficient exploitation of model complementariness by low confidence re-scoring in automatic speech recognition
有权
通过自动语音识别中的低置信度重新评估模型互补性的高效利用
- Patent Title: Efficient exploitation of model complementariness by low confidence re-scoring in automatic speech recognition
- Patent Title (中): 通过自动语音识别中的低置信度重新评估模型互补性的高效利用
-
Application No.: US13518409Application Date: 2010-05-27
-
Publication No.: US09037463B2Publication Date: 2015-05-19
- Inventor: Daniel Willett , Venkatesh Nagesha
- Applicant: Daniel Willett , Venkatesh Nagesha
- Applicant Address: US MA Burlington
- Assignee: Nuance Communications, Inc.
- Current Assignee: Nuance Communications, Inc.
- Current Assignee Address: US MA Burlington
- Agency: Banner & Witcoff, Ltd.
- International Application: PCT/US2010/036319 WO 20100527
- International Announcement: WO2011/149465 WO 20111201
- Main IPC: G10L15/04
- IPC: G10L15/04 ; G10L15/14 ; G10L15/183 ; G10L15/32

Abstract:
A method for speech recognition is described that uses an initial recognizer to perform an initial speech recognition pass on an input speech utterance to determine an initial recognition result corresponding to the input speech utterance, and a reliability measure reflecting a per word reliability of the initial recognition result. For portions of the initial recognition result where the reliability of the result is low, a re-evaluation recognizer is used to perform a re-evaluation recognition pass on the corresponding portions of the input speech utterance to determine a re-evaluation recognition result corresponding to the re-evaluated portions of the input speech utterance. The initial recognizer and the re-evaluation recognizer are complementary so as to make different recognition errors. A final recognition result is determined based on the re-evaluation recognition result if any, and otherwise based on the initial recognition result.
Public/Granted literature
Information query