Speech enhancement using hidden Markov models in Mel-frequency domain

Hidden Markov model (HMM)-based minimum mean square error speech enhancement method in Mel-frequency domain is focused on and a parallel cepstral and spectral (PCS) modeling is proposed. Both Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented for speech enhancement. To estimate clean speech waveform from a noisy signal, an inversion from the Mel-frequency domain to the spectral domain is required which introduces distortion artifacts in the spectrum estimation and the filtering. To reduce the corrupting effects of the inversion, the PCS modeling is proposed. This method performs concurrent modeling in both cepstral and magnitude spectral domains. In addition to the spectrum estimator, magnitude spectrum, log-magnitude spectrum and power spectrum estimators are also studied and evaluated in the HMM-based speech enhancement framework.The performances of the proposed methods are evaluated in the presence of five noise types with different SNR levels and the results are compared with several established speech enhancement methods especially auto-regressive HMM-based speech enhancement. The experimental results for both subjective and objective tests confirm the superiority of the proposed methods in the Mel-frequency domain over the reference methods, particularly for non-stationary noises.

► The HMM-based speech enhancement in cepstral domain is presented.
► A parallel cepstral and spectral (PCS) modeling is proposed.
► Comprehensive evaluations show that proposed techniques outperform well-known methods.
► The PCS modeling results in accurate values for the suppression filters and improves the performance.
► The higher noise reduction rate is achieved for non-stationary noises by the proposed methods.