Non-Linear Filtering for Feature Enhancement of Reverberant Speech

Speaker identification implemented on a mobile robot is a challenging problem because of varying reverberant environments which the robot encounters while in motion. The performance of a typical speaker identification system degrades significantly in reverberant environments. The degradation in performance is mainly due to the conventional feature being not robust to change in reverberant condition. In this paper, we present a non-linear filter based mel frequency cepstral coefficient (MFCC) feature extraction, which is more robust to changes in reverberant conditions. This feature extraction method is a two stage operation and is applied on the spectrogram of the speech signal. The first stage suppresses the frequency spread due to reverberation within each frame and in the second stage, reverberation effect across the frames is suppressed. The performance is evaluated by the GMM-UBM based identifier built and tested with conventional MFCC feature vectors and with the non-linear filter based MFCC feature vectors. We show that, the identification accuracy of GMM-UBM based identifier with non-linear filter based MFCC feature vectors is better than that of conventional MFCC feature vectors.