On the Recognition of Cochlear Implant-Like Spectrally Reduced Speech With MFCC and HMM-Based ASR

This correspondence investigates the recognition of cochlear implant-like spectrally reduced speech (SRS) using mel frequency cepstral coefficient (MFCC) and hidden Markov model (HMM)-based automatic speech recognition (ASR). The SRS was synthesized from subband temporal envelopes extracted from original clean test speech, whereas the acoustic models were trained on a different set of original clean speech signals of the same speech database. It was shown that changing the bandwidth of the subband temporal envelopes had no significant effect on the ASR word accuracy. In addition, increasing the number of frequency subbands of the SRS from 4 to 16 improved significantly the system performance. Furthermore, the ASR word accuracy attained with the original clean speech can be achieved by using the 16-, 24-, or 32-subband SRS. The experiments were carried out by using the TI-digits speech database and the HTK speech recognition toolkit.