Evaluation of quality of speech enhanced by HMM and AR model‐based systems
Speech enhancement algorithms have demonstrated their application potential in a wide variety of speech communication contexts in which the quality or the intelligibility of speech has been degraded by the presence of background noise: hearing aids, cellular phones, public telephones, hands‐free telephones, air‐ground communications, etc. By far the two most popular choices for model‐based speech enhancement are the Wiener‐filter‐based hidden Markov model (HMM), and the autoregressive (AR) model‐based Kalman filter. Although researchers have been studying such enhancement systems for some time, relatively little has been undertaken in comparing the quality of enhanced speech produced by these two systems. This paper studies the Wiener/HMM and AR/KF models and conducts a comprehensive comparative study of the relative quality of enhanced speech, based on utterances from the TIMIT database contaminated by simulated and sampled empirical noises. The Wiener/HMM and AR/KF comparison includes both subjective an...