Gaussian mixture models for spoken language identification

Abstract

Language Identification (LID) is the problem of identifying the language of any spoken utterance irrespective of the topic, speaker or the duration of the speech. Although A very huge amount of work has been done for automatic Language Identification, accuracy and complexity of LID systems remains major challenges. People have used different methods of feature extraction of speech and have used different baseline systems for learning purpose. To understand the role of these issues a comparative study was conducted over few algorithms. The results of this study were used to select appropriate feature extraction method and the baseline system for LID.

Based on the results of the study mentioned above we have used Gaussian Mixture Models (GMM) as our baseline system which are trained using Expectation Maximization (EM) algorithm. Mel Frequency Cepstral Coefficients (MFCC), its delta and delta-delta cepstral coefficients are used as features of speech applied to the system. English and three Indian languages (Hindi, Gujarati and Telugu) are used to test the performances. In this dissertation we have tried to improve the performance of GMM for LID. Two modified EM algorithms are used to overcome the limitations of EM algorithm. The first approach is Split and Merge EM algorithm The second variation is Model Selection Based Self-Splitting Gaussian Mixture Leaning We have also prepared the speech database for three Indian languages namely Hindi, Gujarati and Telugu and that we have used in our experiments.