Affiliations: [a]
Department of Electrical Engineering, FET, Gomal University, Dera Ismail Khan, Pakistan
| [b]
Department of Electrical Engineering, University of Engineering & Technology, Peshawar, Kohat Campus, Pakistan
| [c]
Department of Software Engineering, Bahria University, Islamabad, Pakistan
Correspondence:
[*]
Corresponding author. Nasir Saleem, Department of Electrical Engineering, FET, Gomal University, Dera Ismail Khan, Pakistan. E-mail: [email protected].
Abstract: In real-world situation, speech signals reaching our ears are usually degraded by the background noise. These distortions are detrimental to the speech quality and intelligibility and also cause a serious problem to many speech-related applications, such as automatic speech recognition and speaker identification. In order to deal with the background noise distortions, we propose a strategy to enhance the degraded speech in this paper, where speech enhancement is conducted using supervised deep neural network models. The models are trained to learn a mapping from the features of noisy speech to estimate the ideal-ratio mask (IRM). The estimated IRM is then applied to the noisy speech in order to obtain an enhanced version of the degraded speech. The mean square error (MSE) is used as an objective cost function. Additionally, Global Variance Equalization is performed as a post-processing step to equalize variances of the features. Systematic evaluations and comparisons show that the proposed supervised method improves objective metrics of speech quality and intelligibility substantially and significantly outperforms the competing and baseline speech enhancement methods. Finally, the proposed method is examined in speaker identification task in noisy situations. The proposed method leads to the highest speaker identification rates when compare to the competing and baseline speech enhancement methods.
Keywords: Speech enhancement, deep neural networks, supervised learning, global variance, quality, intelligibility
