  Speech-related applications deliver inferior performance in complex noise
environments. Therefore, this study primarily addresses this problem by
introducing speech-enhancement (SE) systems based on deep neural networks
(DNNs) applied to a distributed microphone architecture, and then investigates
the effectiveness of three different DNN-model structures. The first system
constructs a DNN model for each microphone to enhance the recorded noisy speech
signal, and the second system combines all the noisy recordings into a large
feature structure that is then enhanced through a DNN model. As for the third
system, a channel-dependent DNN is first used to enhance the corresponding
noisy input, and all the channel-wise enhanced outputs are fed into a DNN
fusion model to construct a nearly clean signal. All the three DNN SE systems
are operated in the acoustic frequency domain of speech signals in a
diffuse-noise field environment. Evaluation experiments were conducted on the
Taiwan Mandarin Hearing in Noise Test (TMHINT) database, and the results
indicate that all the three DNN-based SE systems provide the original
noise-corrupted signals with improved speech quality and intelligibility,
whereas the third system delivers the highest signal-to-noise ratio (SNR)
improvement and optimal speech intelligibility.
