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Tree-Structured Clustering Methods for Piecewise Linear-Transformation-Based Noise Adaptation Zhipeng ZHANG Toshiaki SUGIMURA Sadaoki FURUI Publication IEICE TRANSACTIONS on Information and Systems Vol.E88-D No.9 pp.2168-2176 Publication Date: 2005/09/01 Online ISSN: DOI: 10.1093/ietisy/e88-d.9.2168 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Speech and Hearing Keyword: robust speech recognition, noise adaptation, piecewise-linear transformation, tree-structured noise clustering, GMM, Full Text: PDF(891.4KB)>>
Summary: This paper proposes the application of tree-structured clustering to the processing of noisy speech collected under various SNR conditions in the framework of piecewise-linear transformation (PLT)-based HMM adaptation for noisy speech. Three kinds of clustering methods are described: a one-step clustering method that integrates noise and SNR conditions and two two-step clustering methods that construct trees for each SNR condition. According to the clustering results, a noisy speech HMM is made for each node of the tree structure. Based on the likelihood maximization criterion, the HMM that best matches the input speech is selected by tracing the tree from top to bottom, and the selected HMM is further adapted by linear transformation. The proposed methods are evaluated by applying them to a Japanese dialogue recognition system. The results confirm that the proposed methods are effective in recognizing digitally noise-added speech and actual noisy speech issued by a wide range of speakers under various noise conditions. The results also indicate that the one-step clustering method gives better performance than the two-step clustering methods. | ||||||||||
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