Development and use of a new Speech Quality Evaluation Parameter ESNR using ANN and Grey Wolf Optimizer

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The performance of Speech Enhancement (SE) Algorithms is evaluated using various objective and subjective evaluation parameters. Recently, few objective evaluation parameters are developed for the measurement of speech quality and intelligibility. But still, there are ample scopes determining statistical parameters to predict the SNR of a noisy speech signal without using any reference of clean signal and noise. In this paper, this problem has been addressed and three types of Artificial Neural Networks (ANN) are developed for efficient prediction of the estimated SNR (E-SNR) of a given noisy speech signal. To further improve the accuracy of prediction of the SNR of the ANN, the coefficients of ANN are tuned using the bio-inspired optimization technique. In this paper, a popular and efficient Grey wolf Optimization is chosen for the purpose. Several audio features are studied and appropriate features are chosen as the inputs to the ANN. Finally, a comparative performance analysis is carried out using two standard speech databases and the best performing ANN and audio features are identified to provide the best ESNR.