Modeling of linguistic and acoustic information from speech signal for multilingual spoken language identification system (SLID)

Abstract

Spoken language identification is the task of identifying a language from the given speech signal. Efforts to develop language identification systems for Indian languages have been very limited due to the problem of speaker availability and language legibility but the requirement of SLID is increasing for civil and defense applications day by day. The present paper reports a study to develop a multilingual identification system for two Indian languages i.e. Hindi and Manipuri by using PPRLM approach that requires phoneme based labeled speech corpus for each language. For each language, data set of 300 phonetically rich sentences spoken by 25 native speakers (15000 utterances) were recorded, analyzed and annotated phonemically to make trigram based phonotactic model. The features of the speech signal have been extracted using MFCCs and GMM was used as a classifier. Results show that accuracy increases with the increase of Gaussians and also with the training samples.