Auditory-modeling inspired methods of feature extraction for robust automatic speech recognition
This paper proposes a technique of extracting robust feature vectors for ASR. The technique is inspired by work related to auditory modeling. It involves first filtering the speech signal through a bank of band-pass filters, which are based on a model of the human cochlea. Autocorrelation functions (ACF) are computed on the filters' outputs. Then the individual ACFs are scaled by their corresponding voice indices (VIs), which use information related to the pitch. A summed ACF is then obtained by summing the individual ACFs across the bands. Feature vectors are then computed using standard cepstral analysis, by treating the summed ACF as a regular ACF. Finally, frame indices (FIs) weigh the feature vectors in the time domain. The effectiveness of the proposed techniques, compared to LPCC and MFCC, are demonstrated by comparing the results obtained from simple recognition experiments.