Language boundary detection and identification of mixed-language speech based on map estimation

Chi Jiun Shia, Yu Hsien Chiu, Jia Hsin Hsieh, Chung Hsien Wu

Research output: Contribution to journalConference articlepeer-review

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
Publication statusPublished - 2004 Sep 28
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 2004 May 172004 May 21

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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