Comparison of two phonetic approaches to language identification
François Pellegrino, Jérôme Farinas, Régine Andre-Obrecht · 1999
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 auto-matically detected segments. In the Phonetic Differenti-ated 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 seg-ments. For each approach, no labeled data are required. GMMs are initialized using an efficient data-driven variant of the LBG algorithm: the LBG-Rissanen algo-rithm. 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 dura-tion utterances for the male speakers. We increase this performance (91%) when we merge the two systems.