A Fully Connected Deep Neural Network approach with multiple sub-frame consideration and phase recompense for noise suppression
preprintposted on 14.08.2021, 16:06 by Rohun NisaRohun Nisa
In the speech communication process, the desirable speech needs to be addressed under the influence of noise encountered in diverse environments that degrade the speech quality and intelligibility. In opposition to the unfavorable scenario particularly lowered signal-to-noiseratio, the progress of traditional noise suppressive algorithms is hindered, introducing further distortion in speech, making them non-applicable for real-time applications. In order to reduce the complicacies of current algorithms, a hybrid approach for upgrading the quality together with intelligibility of speech is proposed for dealing with real-world hearing scenario. For improving the intelligibility of speech of interest, multiple sub-frame analysis using over-spectral subtractive factor with phase recompense approach is implemented on the multi-channel noise corrupted speech, yielding approximated speech spectrum, that constitutes the pre-processing stage. The approximated speech spectrum and clean speech spectrum forming the training set are further fed to Fully Connected Layered Deep Neural Network to reduce the mean square error with the incorporation of regression network resulting in improved quality for speech. The proposed hybrid network results in upgraded intelligibility and quality in speech signal with improved SNR measured in terms of Short-Time-Objective-Intelligibility (STOI) score, Perceptual-Evaluation-of-Speech-Quality (PESQ) score, Segmental SNR level, and Mean Square Error (MSE) in contrast to prior noise suppressive algorithms together with less complexity of the hybrid network.