• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.383-389
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• 2004

In this paper. we propose the bias compensation methods and the eigenvoice method using the mean of dimensional eigenvoice to improve the performance of rapid speaker adaptation based on eigenvoice under mismatch between training and test environment. Experimental results for vocabulary-independent word recognition task (using PBW 452 DB) show that the proposed methods yield improvements for small adaptation data. We obtained about 22∼30% relative improvement by the bias compensation methods as amount of adaptation data varied from 1 to 50, and obtained 41% relative improvement in error rate by the eigenvoice method using the mean of dimensional eigenvoice with only single adaptation word.

• The Journal of the Acoustical Society of Korea
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• v.22 no.5
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• pp.403-407
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• 2003

Recently, eigenvoice approach has been widely used for rapid speaker adaptation. However, even in the eigenvoice approach, Performance improvement using very small amount of adaptation data is relatively small in comparison with that using somewhat large adaptation data because the reliable estimation of weights of eigenvoice is difficult. In this paper, we propose a rapid speaker adaptation method based on eigenvoice using the weight distribution characteristics to improve the performance on a small adaptation data. In the Experimental results on vocabulary-independent word recognition task (using PBW 452 database), the weight threshold method alleviates the problem of relatively low performance for a tiny small adaptation data. When single adaptation word is used, word error rate is reduced about 9-18% by the weight threshold method.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.33-36
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• 2000

어휘독립 고립단어인식은 미리 훈련된 부단어(sub-word) 단위의 음향모델을 이용하여 수시로 변하는 인식대상어휘를 인식하는 것이다. 본 논문에서는 소용량 음성 데이터베이스를 이용하여 어휘독립음성인식 시스템을 구성하였다. 소용량 음성 데이터베이스에서 미관측문맥 종속형 부단어에 대한 처리에 효과적인 백오프 기법을 이용한 음소 군집화 방법으로 문턱값을 변화시키며 인식실험을 수행하였다. 그리고 훈련용 데이터의 부족으로 인하여 문맥 종속형 부단어 모델이 훈련용 데이터베이스로 편중되는 문제를 deleted interpolation 방법을 이용하여 문맥 종속형 부단어 모델과 문맥 독립형 부단어 모델을 병합함으로써 해결하였다. 그 결과 음성인식의 성능이 향상되었다.