Back to Search
Start Over
Automatic Segmentation and Identification of Mixed-Language Speech Using Delta-BIC and LSA-Based GMMs.
- Source :
- IEEE Transactions on Audio, Speech & Language Processing; Jan2006, Vol. 14 Issue 1, p266-276, 11p, 4 Diagrams, 2 Charts, 6 Graphs
- Publication 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15587916
- Volume :
- 14
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Audio, Speech & Language Processing
- Publication Type :
- Academic Journal
- Accession number :
- 23172990
- Full Text :
- https://doi.org/10.1109/TSA.2005.852992