Automatic Noise Class Detection For Improving Speech Quality Using Artificial Neural Networks

Abstract

Speech quality degrades in noisy environments while using a particular voice over internet protocol (VoIP) application, for example, Microsoft Skype, Apple Facetime, etc. Speech enhancement algorithms separate noise from the degraded speech. No-reference speech quality metric (SQM), such as P.563, measures the quality of speech. To this end, this research study develops a novel approach for extracting features from the noisy speech samples available from the NOIZEUS corpus for detecting the noise class (noise type and SNR) using deep neural network (DNN). It integrates speech enhancement algorithms with SQM to estimate the speech quality (MOS score) from the noisy samples, which is then used as a feature vector to train a DNN. Results demonstrate that the DNN outperforms in detecting the noise class as compared to the machine learning classifiers, tested with different noise classes. This suggests to develop a noise-sensitive speech quality prediction model for real-time measuring and monitoring of the quality of speech.