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An unsupervised approach to language identification

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2 Author(s)
F. Pellegrino ; IRIT, Toulouse, France ; R. Andre-Obrecht

This paper presents an unsupervised approach to automatic language identification (ALI) based on vowel system modeling. Each language vowel system is modeled by a Gaussian mixture model (GMM) trained with automatically detected vowels. Since this detection is unsupervised and language independent, no labeled data are required. GMMs are initialized using an efficient data-driven variant of the LBG algorithm: the LBG-Rissanen (1983) algorithm. With 5 languages from the OGI MLTS corpus and in a close set identification task, we reach 79% of correct identification using only the vowel segments detected in 45 second duration utterances for the male speakers

Published in:

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on  (Volume:2 )

Date of Conference:

15-19 Mar 1999