Model-Based Speech Enhancement in the Modulation Domain
This work addresses speech enhancement for applications like hearing aids or communication systems, but it is incremental as it builds on existing modulation-domain methods with a novel statistical model.
The paper tackles speech enhancement by proposing a modulation-domain algorithm using a Kalman filter and a Gaussring model to jointly estimate speech and noise dynamics, resulting in consistent improvements in speech quality across various SNRs and good performance in speech recognition for two noise types.
This paper presents an algorithm for modulation-domain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical "Gaussring" model that comprises a mixture of Gaussians whose centers lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality measure, segmental SNR measure, and short-time objective intelligibility measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring-model-based algorithm performs well for two types of noise.