Novel Gammatone Filterbank Based Spectro-Temporal Features for Robust Phoneme Recognition

Abstract

Recently, Automatic Speech Recognition (ASR) technology is being used in practical scenarios and hence, robustness of ASR is becoming increasingly important. State-of-the-art Mel Frequency Cepstral Coefficients (MFCC) features are known to be affected by acoustic noise whereas physiologically motivated features such as spectro-temporal Gabor filterbank (GBFB) features intend to perform better in signal degradation conditions. The spectro-temporal GBFB feature extraction incorporates mel filterbank to mimic frequency mapping in the Basilar Membrane (BM) in the inner ear. In this paper, Gammatone filterbank is used and a comparison is done between GBFB with mel filterbank (GBFBmel) features and GBFB with Gammatone filterbank (GBFBGamm) features. MFCC features and Gammatone Frequency Cepstral Coefficients (GFCC) features are concatenated with GBFBmel and GBFBGamm features, respectively, to improve recognition performance. Experiments are carried out to calculate phoneme recognition accuracy (PRA), on TIMIT database (without ‘sa’ sentences), with additive white, volvo and high frequency noises at various SNR levels from −5 dB to 20 dB. Results show that, with acoustic modeling only, proposed feature set (GBFBGamm+GFCC) performs better (in terms of PRA %), than GBFBmel+MFCC features by an average of 1%, 0.2% and 0.8% for white, volvo and high frequency noises, respectively.