Language identification using phase information

The present work investigates the importance of phase in language identification (LID). We have proposed three phase based features for the language recognition task. In this work, auto-regressive model with scale factor error augmentation have been used for better representation of phase based features. We have developed three group delay based systems, namely, normal group delay based system, auto-regressive model group delay based system and auto-regressive group delay with scale factor augmentation based system. As mel-frequency cepstral coefficients (MFCCs) are extracted from the magnitude of the Fourier transform, we have combined this MFCC-based system with our phase-based systems to exploit the complete information contained in a speech signal. In this work, we have used IITKGP-MLILSC speech database and OGI Multi-language Telephone Speech (OGI-MLTS) corpus for our experiments. We have used Gaussian mixture models for building the language models. From the experimental results it is observed that the LID accuracy obtained from our proposed phase based features is comparable with MFCC features. We have also observed some performance improvement in the LID accuracy on combining the proposed phase-based systems with the state of the art MFCC-based system.