Cepstrum-based filter-bank design using discriminative feature extraction training at various levels

This paper investigates the realization of optimal filter bank-based cepstral parameters. The framework is the discriminative feature extraction method (DFE) which iteratively estimates the filter-bank parameters according to the errors that the system makes. Various parameters of the filter-bank, such as center frequency, bandwidth, and gain are optimized using a string-level optimization and a frame-level optimization scheme. Application to vowel and noisy telephone speech recognition tasks shows that the DFE method realizes a more robust classifier by appropriate feature extraction