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- Title
Minimum mean square error estimator for speech enhancement in additive noise assuming Weibull speech priors and speech presence uncertainty.
- Authors
Bahrami, Mojtaba; Faraji, Neda
- Abstract
A novel single-channel technique was proposed based on a minimum mean square error (MMSE) estimator to enhance short-time spectral amplitude (STSA) in the Discrete Fourier Transform (DFT) domain. In the present contribution, a Weibull distribution was used to model DFT magnitudes of clean speech signals under the additive Gaussian noise assumption. Moreover, the speech enhancement procedure was conducted with (WSPU) and without speech presence uncertainty (WoSPU). The theoretical spectral gain function was obtained as a weighted geometric mean of hypothetical gains associated with signal presence and absence. Extensive experiments were conducted with clean speech signals taken from the TIMIT database, which had been degraded by various additive non-stationary noise sources, and then enhanced signals were evaluated. The evaluation results demonstrated the outperformance of the proposed method compared to the probability density functions (PDF) of Rayleigh and Gamma distributions in terms of segmental signal-to-noise ratio (segSNR), general SNR, and perceptual evaluation of speech quality (PESQ). The performance in the WSPU case was also significantly improved compared to WoSPU, assuming Weibull speech priors in the MMSE-STSA based speech enhancement algorithm.
- Subjects
SPEECH enhancement; WEIBULL distribution; DISCRETE Fourier transforms; PROBABILITY density function; RAYLEIGH model; GAMMA distributions
- Publication
International Journal of Speech Technology, 2021, Vol 24, Issue 1, p97
- ISSN
1381-2416
- Publication type
Article
- DOI
10.1007/s10772-020-09767-y