Language identification using parallel syllable-like unit recognition

Automatic spoken language identification (LID) is the task of identifying the language from a short utterance of the speech signal. The most successful approach to LID uses phone recognizers of several languages in parallel. The basic requirement to build Parallel Phone recognition (PPR) system is annotated corpora. In this paper, a novel approach is proposed for the LID task which uses parallel syllablelike unit recognizers, in a frame work similar to PPR approach in the literature. The difference is that unsupervised syllable models are built from the training data. The data is first segmented into syllable-like units. The syllable segments are then clustered using an incremental approach. This results in a set of syllable models for each language. Our initial results on OGI_MLTS corpora show that the performance is 69.5%. We further show that if only a subset of syllable models that are unique (in some sense), are considered, the performance improves to 75.9%.