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

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.