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Spectral Domain Speech Enhancement Using HMM State-Dependent Super-Gaussian Priors

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3 Author(s)
Mohammadiha, N. ; Sch. of Electr. Eng., KTH R. Inst. of Technol., Stockholm, Sweden ; Martin, R. ; Leijon, A.

The derivation of MMSE estimators for the DFT coefficients of speech signals, given an observed noisy signal and super-Gaussian prior distributions, has received a lot of interest recently. In this letter, we look at the distribution of the periodogram coefficients of different phonemes, and show that they have a gamma distribution with shape parameters less than one. This verifies that the DFT coefficients for not only the whole speech signal but also for individual phonemes have super-Gaussian distributions. We develop a spectral domain speech enhancement algorithm, and derive hidden Markov model (HMM) based MMSE estimators for speech periodogram coefficients under this gamma assumption in both a high uniform resolution and a reduced-resolution Mel domain. The simulations show that the performance is improved using a gamma distribution compared to the exponential case. Moreover, we show that, even though beneficial in some aspects, the Mel-domain processing does not lead to better results than the algorithms in the high-resolution domain.

Published in:

Signal Processing Letters, IEEE  (Volume:20 ,  Issue: 3 )