NDLI: Automatic segmentation and identification of mixed-language speech using delta-BIC and LSA-based GMMs

Content Provider	IEEE Xplore Digital Library
Author	Chung-Hsien Wu Yu-Hsien Chiu Chi-Jiun Shia Chun-Yu Lin
Copyright Year	2006
Abstract	This paper proposes an approach to segmenting and identifying mixed-language speech. A delta Bayesian information criterion (delta-BIC) is firstly applied to segment the input speech utterance into a sequence of language-dependent segments using acoustic features. A VQ-based bi-gram model is used to characterize the acoustic-phonetic dynamics of two consecutive codewords in a language. Accordingly the language-specific acoustic-phonetic property of sequence of phones was integrated in the identification process. A Gaussian mixture model (GMM) is used to model codeword occurrence vectors orthonormally transformed using latent semantic analysis (LSA) for each language-dependent segment. A filtering method is used to smooth the hypothesized language sequence and thus eliminate noise-like components of the detected language sequence generated by the maximum likelihood estimation. Finally, a dynamic programming method is used to determine globally the language boundaries. Experimental results show that for Mandarin, English, and Taiwanese, a recall rate of 0.87 for language boundary segmentation was obtained. Based on this recall rate, the proposed approach achieved language identification accuracies of 92.1% and 74.9% for single-language and mixed-language speech, respectively.
Sponsorship	IEEE Signal Processing Society
Starting Page	266
Ending Page	276
Page Count	11
File Size	771450
File Format	PDF
ISSN	15587916
Volume Number	14
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Natural languages Speech analysis Application software Principal component analysis Bayesian methods Filtering Noise generators Maximum likelihood detection Maximum likelihood estimation Dynamic programming single-language speech Gaussian mixture model language identification latent semantic analysis mixed-language speech
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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