Parallel inference of dirichlet process Gaussian mixture models for unsupervised acoustic modeling: A feasibility study

Hongjie Chen, Cheung Chi Leung, Lei Xie, Bin Ma, Haizhou Li

科研成果: 期刊稿件会议文章同行评审

65 引用 (Scopus)

摘要

We adopt a Dirichlet process Gaussian mixture model (DPGMM) for unsupervised acoustic modeling and represent speech frames with Gaussian posteriorgrams. The model per- forms unsupervised clustering on untranscribed data, and each Gaussian component can be considered as a cluster of sounds from various speakers. The model infers its model complex- ity (i.e. The number of Gaussian components) from the data. For computation efficiency, we use a parallel sampler for the model inference. Our experiments are conducted on the corpus provided by the zero resource speech challenge. Experimental results show that the unsupervised DPGMM posteriorgrams obviously outperform MFCC, and perform comparably to the posteriorgrams derived from language-mismatched phoneme recognizers in terms of the error rate of ABX discrimination test. The error rates can be further reduced by the fusion of these two kinds of posteriorgrams.

源语言英语
页(从-至)3189-3193
页数5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2015-January
出版状态已出版 - 2015
活动16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, 德国
期限: 6 9月 201510 9月 2015

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