  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.
