Significance of Accuracy in Vowel Region Detection for Robust Language Identification

In this study, a robust language identification (LID) system is investigated with vowel region detection scheme incorporated at the front-end. The background noise is known to severely reduce language identification performance. In this work, Mel-frequency cepstral features extracted from the vowel regions instead of voiced speech are utilized for language identification system modeling. Experimentation is done using different vowel region detection algorithms on deep neural networks (DNN) and deep neural networks with attention (DNN-WA) based LID systems on IIITH Indian language dataset. Experimental results show that the accurate vowel region detection mechanism at the front-end of a LID system improved the performance in noisy environment.