Spectral Domain Speech Enhancement Using HMM State-Dependent Super-Gaussian Priors
2013 (English)In: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 20, no 3, 253-256 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013. Vol. 20, no 3, 253-256 p.
HMM, speech enhancement, super-Gaussian pdf
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:kth:diva-118490DOI: 10.1109/LSP.2013.2242467ISI: 000314828600002ScopusID: 2-s2.0-84873620144OAI: oai:DiVA.org:kth-118490DiVA: diva2:606567
QC 201302212013-02-212013-02-192013-09-16Bibliographically approved