This paper proposes a Maximum a Posteriori (MAP) based approach to jointly segment and identify an utterance with mixed languages. A statistical framework for language boundary detection and language identification is proposed. First, the MAP estimation is used to determine the boundary number and positions. Further, an LSA-based GMM and a VQ-based bi-gram language model are proposed to characterize a language and used for language identification. Finally, a likelihood ratio test approach is used to determine the optimal number of language boundaries. Experimental results show that the proposed approach exhibits encouraging potential in mixed-language segmentation and identification.
|期刊||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|出版狀態||Published - 2004 九月 28|
|事件||Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada|
持續時間: 2004 五月 17 → 2004 五月 21