  In this paper, we propose a novel speech enhancement (SE) method by
exploiting the discrete wavelet transform (DWT). This new method reduces the
amount of fast time-varying portion, viz. the DWT-wise detail component, in the
spectrogram of speech signals so as to highlight the speech-dominant component
and achieves better speech quality. A particularity of this new method is that
it is completely unsupervised and requires no prior information about the clean
speech and noise in the processed utterance. The presented DWT-based SE method
with various scaling factors for the detail part is evaluated with a subset of
Aurora-2 database, and the PESQ metric is used to indicate the quality of
processed speech signals. The preliminary results show that the processed
speech signals reveal a higher PESQ score in comparison with the original
counterparts. Furthermore, we show that this method can still enhance the
signal by totally discarding the detail part (setting the respective scaling
factor to zero), revealing that the spectrogram can be down-sampled and thus
compressed without the cost of lowered quality. In addition, we integrate this
new method with conventional speech enhancement algorithms, including spectral
subtraction, Wiener filtering, and spectral MMSE estimation, and show that the
resulting integration behaves better than the respective component method. As a
result, this new method is quite effective in improving the speech quality and
well additive to the other SE methods.
