Spectro-temporal Gabor features for speaker recognition

In this work, we have investigated the performance of 2D Gabor features (known as spectro-temporal features) for speaker recognition. Gabor features have been used mainly for automatic speech recognition (ASR), where they have yielded improvements. We explored different Gabor feature implementations, along with different speaker recognition approaches, on ROSSI [1] and NIST SRE08 databases. Using the noisy ROSSI database, the Gabor features performed as well as the MFCC features standalone, and score-level combination of Gabor and MFCC features resulted in an 8% relative EER improvement over MFCC features standalone. These results demonstrated the value of both spectral and temporal information for feature extraction, and the complementarity of Gabor features to MFCC features.