Sixth European Conference on Speech Communication and Technology
(EUROSPEECH'99)

Budapest, Hungary
September 5-9, 1999

Comparison of Two Phonetic Approaches to Language Identification

François Pellegrino, Jérôme Farinas, Régine André-Obrecht

IRIT University Paul Sabatier, Toulouse, France

This paper presents two unsupervised approaches to Automatic Language Identification (ALI) based on a segmental preprocessing. In the Global Segmental Model approach, the language system is modeled by a Gaussian Mixture Model (GMM) trained with automatically detected segments. In the Phonetic Differentiated Model approach, an unsupervised detection vowel/non vowel is performed and the language model is defined with two GMMs, one to model the vowel segments and a second one to model the others segments. For each approach, no labeled data are required. GMMs are initialized using an efficient data-driven variant of the LBG algorithm: the LBG-Rissanen algorithm. With 5 languages from the OGI MLTS corpus and in a closed set identification task, we reach 85 % of correct identification with each system using 45 second duration utterances for the male speakers. We increase this performance (91%) when we merge the two systems.


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Bibliographic reference.  Pellegrino, François / Farinas, Jérôme / André-Obrecht, Régine (1999): "Comparison of two phonetic approaches to language identification", In EUROSPEECH'99, 399-402.