Single-channel speech enhancement by using psychoacoustical model inspired fusion framework

TL;DR

This paper proposes a fusion framework combining acoustic and modulation domain approaches for single-channel speech enhancement, improving perceived quality and intelligibility in noisy conditions.

Contribution

It introduces a novel fusion framework that leverages psychoacoustic models to enhance speech quality and intelligibility, overcoming limitations of individual methods.

Findings

Consistent improvements in speech quality and intelligibility across noise conditions.

Effective noise reduction at high frequencies with reduced speech distortion.

Superior performance compared to baseline techniques.

Abstract

When the parameters of Bayesian Short-time Spectral Amplitude (STSA) estimator for speech enhancement are selected based on the characteristics of the human auditory system, the gain function of the estimator becomes more flexible. Although this type of estimator in acoustic domain is quite effective in reducing the back-ground noise at high frequencies, it produces more speech distortions, which make the high-frequency contents of the speech such as friciatives less perceptible in heavy noise conditions, resulting in intelligibility reduction. On the other hand, the speech enhancement scheme, which exploits the psychoacoustic evidence of frequency selectivity in the modulation domain, is found to be able to increase the intelligibility of noisy speech by a substantial amount, but also suffers from the temporal slurring problem due to its essential design constraint. In order to achieve the joint improvements in both the perceived speech quality and intelligibility, we proposed and investigated a fusion framework by combining the merits of acoustic and modulation domain approaches while avoiding their respective weaknesses. Objective measure evaluation shows that the proposed speech enhancement fusion framework can provide consistent improvements in the perceived speech quality and intelligibility across different SNR levels in various noise conditions, while compared to the other baseline techniques.