Adaptation of automatic speech recognizers to new speakers using canonical correlation analysis techniques
✍ Scribed by K. Choukri; G. Chollet
- Publisher
- Elsevier Science
- Year
- 1986
- Tongue
- English
- Weight
- 574 KB
- Volume
- 1
- Category
- Article
- ISSN
- 0885-2308
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✦ Synopsis
This paper describes various speaker normalization and adaptation techniques of a knowledge data base or reference templates to new speakers in automatic speech recognition (ASR). It focuses on a technique for learning spectral transformations, based on a statistical-analysis tool (canonical correlation analysis), to adapt a standard dictionary to arbitrary speakers. The proposed method should permit to improve speaker independence in large vocabulary ASR. Application to an isolated word recognizer improved a 70% correct score to 87%.
A dynamic aspect of the speaker adaptation procedure is introduced and evaluated in a particular strategy.