Automatic language identification: an alternative approach to phonetic modelling

Abstract

This paper deals with our research on vowel system modelling in an automatic language identification (LID) purpose. The study of vowel systems shows that they carry an important part of the language characteristics. Taking advantage of this knowledge is promising. We propose an alternative modelling to the standard acoustic–phonetic decoding currently used as front-end in the LID systems: each language vocalic system is modelled by a Gaussian mixture, estimated out from automatically detected vowels. OGI multi-lingual telephone speech (MLTS) corpus is used to assess this approach. In a five language close set identification task, we reach 57.3% of correct identification with 45 s duration utterances. Taking into account that we use only the vowel information (on average in our database, 45 s of speech consist of less than 25 s of vowels, 10 s of consonants and 10–15 s of silence) and no language modelling, these results are very promising and offer many perspectives.