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KMID : 1022420140060010077
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Phonetics and Speech Sciences
2014 Volume.6 No. 1 p.77 ~ p.83
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Automatic Clustering of Speech Data Using Modified MAP Adaptation Technique
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Ban Sung-Min
Kang Byung-Ok
Kim Hyung-Soon
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Abstract
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This paper proposes a speaker and environment clustering method in order to overcome the degradation of the speechrecognition performance caused by various noise and speaker characteristics. In this paper, instead of using the distancebetween Gaussian mixture model (GMM) weight vectors as in the Google’s approach, the distance between the adapted meanvectors based on the modified maximum a posteriori (MAP) adaptation is used as a distance measure for vector quantization(VQ) clustering. According to our experiments on the simulation data generated by adding noise to clean speech, theproposed clustering method yields error rate reduction of 10.6% compared with baseline speaker-independent (SI) model,which is slightly better performance than the Google's approach.
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KEYWORD
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speech recognition, speech data clustering, KL divergence, MAP adaptation
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