IMPROVING AUTOREGRESSIVE HIDDEN MARKOV MODEL RECOGNITION ACCURACY USING A NON-LINEAR FREQUENCY SCALE WITH APPLICATION TO SPEECH ENHANCEMENT
B. T. Logan and A. J. Robinson
A new method to improve the accuracy of Autoregressive Hidden Markov Model (AR-HMM) based recognition systems is proposed. The technique uses the bilinear transform to warp the frequency scale of the observation vectors, hence it uses a better perceptual measure to compare the observation vectors to the trained models. Results presented for the E-set letters from the ISOLET database and the first speaker dependent task of the Resource Management (RM) database show that this technique improves recognition accuracy considerably. However, in the case of the RM system, the recognition results still fall short of those obtained from a similar mel-frequency cepstral (MFCC) based system without delta parameters. Reasons for the inferior performance of the AR-HMM system are proposed and future research directions are suggested. The models built for the RM task are incorporated into an existing enhancement algorithm to form a large vocabulary speaker dependent enhancement system. Preliminary results are presented for this system.
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