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학술지 Combining Multiple Acoustic Models in GMM Spaces for Robust Speech Recognition
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저자
강병옥, 권오욱
발행일
201603
출처
IEICE Transactions on Information and Systems, v.E99.D no.3, pp.724-730
ISSN
1745-1361
출판사
일본, 전자정보통신학회 (IEICE)
DOI
https://dx.doi.org/10.1587/transinf.2015EDP7252
협약과제
15MS9500, 언어학습을 위한 자유발화형 음성대화처리 원천기술 개발, 이윤근
초록
We propose a new method to combine multiple acoustic models in Gaussian mixture model (GMM) spaces for robust speech recognition. Even though large vocabulary continuous speech recognition (LVCSR) systems are recently widespread, they often make egregious recognition errors resulting from unavoidable mismatch of speaking styles or environments between the training and real conditions. To handle this problem, a multi-style training approach has been used conventionally to train a large acoustic model by using a large speech database with various kinds of speaking styles and environment noise. But, in this work, we combine multiple sub-models trained for different speaking styles or environment noise into a large acoustic model by maximizing the log-likelihood of the sub-model states sharing the same phonetic context and position. Then the combined acoustic model is used in a new target system, which is robust to variation in speaking style and diverse environment noise. Experimental results show that the proposed method significantly outperforms the conventional methods in two tasks: Non-native English speech recognition for second-language learning systems and noise-robust point-of-interest (POI) recognition for car navigation systems.
키워드
Acoustic model, GMM combination, Noise-robust speech recognition, Non-native speech recognition
KSP 제안 키워드
Car navigation, Conventional methods, Gaussian mixture Model(GMM), Language Learning, Learning System, Log-likelihood, Noise robust speech recognition, Non-native speech, Point of interest, Speech Database, Sub-models