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A deep neural network-correlation phase sensitive mask based estimation to improve speech intelligibility
Author(s)
Date Issued
September 1, 2023
Type
Article
Abstract
General masking-based speech enhancement using a deep learning architecture (DNN) approach focuses on the spectral values of the speech in order to show improvement in intelligibility. But, the residual noise present in the phase spectrum and inter-channel correlation dependency between noise and noisy speech can impact the results of intelligibility in speech enhancement. This research work proposes a correlation phase-sensitive novel mask which contains phase, magnitude spectral and inter-channel correlation for a tangible improvement in speech intelligibility. The correlation parameter finds the dependency between the signals and phase spectrum factor eliminates the residual noise. In addition, selecting the prior features from the feature combination also helps in reducing the dimensionality and increases the accuracy of the enhanced speech. This work also aims to decrease the complexity of the DNN by analysing the network with different parameters. The performance of the mask is evaluated with various intelligibility factors. The proposed mask has been compared with different mask estimators. The proposed mask has generated estimated speech with an increase in the intelligibility of 0.02-0.001 over six different noises and four different signal-to-noise (SNR) levels.
Citation
Applied Acoustics, 212, 2023