A Novel Approach to Improve the Speech Intelligibility Using Fractional Delta-amplitude Modulation Spectrogram. Issue 7 (17th November 2018)

• Record Type: Journal Article
• Authors: Haridas, Arul Valiyavalappil; Marimuthu, Ramalatha; Chakraborty, Basabi
• Is Part Of: Cybernetics and systems. Volume 49:Issue 7/8(2018)
• Page Start: 421
• Page End: 451
• Publication Date: 2018-11-17
• Subjects: AMS feature extraction -- deep belief networks -- fractional calculus -- Hanning window -- speech enhancement; Cybernetics -- Periodicals; System theory -- Periodicals; 003.5
• DOI: 10.1080/01969722.2018.1448241
• Languages: English
• ISSNs: 0196-9722

Abstract

Abstract: Speech enhancement is an interesting research area that aims at improving the quality and intelligibility of the speech that is affected by the additive noises, such as airport noise, train noise, restaurant noise, and so on. The presence of these background noises degrades the comfort of listening of the end user. This article proposes a speech enhancement method that uses a novel feature extraction which removes the noise spectrum from the noisy speech signal using a novel fractional delta-AMS (amplitude modulation spectrogram) feature extraction and the D-matrix feature extraction method. The fractional delta-AMS feature extraction strategy is the modification of the delta-AMS with the fractional calculus that increases the sharpness of the feature extraction. The extracted features from the frames are used to determine the optimal mask of all the frames of the noisy speech signal and the mask is employed for training the deep belief neural networks (DBN). The two metrics root mean square error (RMSE) and perceptual evaluation of speech quality (PESQ) are used to evaluate the method. The proposed method yields a better value of PESQ at all level of noise and RMSE decreases with increased noise level.