Discriminative Training Techniques for Acoustic Language Identification

This paper presents comparison of maximum likelihood (ML) and discriminative maximum mutual information (MMI) training for acoustic modeling in language identification (LID). Both approaches are compared on state-of-the-art shifted delta-cepstra features, the results are reported on data from NIST 2003 evaluations. Clear advantage of MMI over ML training is shown. Further improvements of acoustic LID are discussed: heteroscedastic linear discriminant analysis (HLDA) for feature de-correlation and dimensionality reduction and ergodic hidden Markov models (EHMM) for better modeling of dynamics in the acoustic space. The final error rate compares favorably to other results published on NIST 2003 data