Sparse representation based language identification using prosodic features for Indian languages

This work explores the use of prosodic feature based sparse representation classification (SRC) system for language identification (LID) task. The prosodic features are computed by extracting syllable like unit with the help of a vowel onset points detection algorithm and mapped to i-vector domain for SRC using an exemplar dictionary. This work is a motivation from recently reported LID approach using low-dimensional i-vectors. The experiments are performed on a locally collected dataset consisting of five Indian languages. On comparing the SRC system performance with that of a contrast system based on cosine distance scoring (CDS), it is noted that the former one performs significantly better than the latter one. The performance of the best system with session/channel compensation in terms of equal error rate and minimum detection cost function turns out to be 7.46% and 0.1338, respectively.