- Patent Title: Deep language and acoustic modeling convergence and cross training
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Application No.: US15471436Application Date: 2017-03-28
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Publication No.: US11270686B2Publication Date: 2022-03-08
- Inventor: Aaron K. Baughman , John M. Ganci, Jr. , Stephen C. Hammer , Craig M. Trim
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Stephen J. Walder, Jr; Anthony V. England
- Main IPC: G10L15/06
- IPC: G10L15/06 ; G10L15/01 ; G10L25/51 ; G10L15/183 ; G10L15/18 ; G10L15/30 ; G10L15/16

Abstract:
A model-pair is selected to recognize spoken words in a speech signal generated from a speech, which includes an acoustic model and a language model. A degree of disjointedness between the acoustic model and the language model is computed relative to the speech by comparing a first recognition output produced from the acoustic model and a second recognition output produced from the language model. When the acoustic model incorrectly recognizes a portion of the speech signal as a first word and the language model correctly recognizes the portion of the speech signal as a second word, a textual representation of the second word is determined and associated with a set of sound descriptors to generate a training speech pattern. Using the training speech pattern, the acoustic model is trained to recognize the portion of the speech signal as the second word.
Public/Granted literature
- US20180286386A1 DEEP LANGUAGE AND ACOUSTIC MODELING CONVERGENCE AND CROSS TRAINING Public/Granted day:2018-10-04
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