NDLI: Optimizing the acoustic modeling from an unbalanced bi-lingual corpus

Content Provider	IEEE Xplore Digital Library
Author	Dau-cheng Lyu Ren-yuan Lyu
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Chang Gung Univ., Taoyuan (Dau-cheng Lyu)
Abstract	Phoneme set clustering of accurate modeling is important in the task of multilingual speech recognition, especially when each of the available language training corpora is mismatched, such as is the case between a major language, like Mandarin, and a minor language, like Taiwanese. In this paper, we present a data-driven approach for not only acquiring a proper phoneme set but optimizing the acoustic modeling in this situation. In order to obtain the phoneme set that is suitable for the unbalanced corpus, we use an agglomerative hierarchical clustering with delta Bayesian information criteria. Then for training each of the acoustic models, we choose a parametric modeling technique, model complexity selection, to adjust the number of mixtures for optimizing the acoustic model between the new phoneme set and the available training data. The experimental results are very encouraging in that the proposed approach reduces relative syllable error rate by 7.8% over the best result of the knowledge-based approach.
Starting Page	4301
Ending Page	4304
File Size	259908
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4518606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Natural languages Speech recognition Training data Acoustic measurements Bayesian methods Large-scale systems Computer science Acoustical engineering Parametric statistics Error analysis multilingual speech recognition phoneme set clustering delta-BIC
Content Type	Text
Resource Type	Article

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