Automatic Speech Intelligibility Detection for Speakers with Speech Impairments: The Identification of Significant Speech Features

Rosdi, Fadhilah and Mustafa, Mumtaz Begum and Salim, Siti Salwah and Mat Zin, Nor Azan (2019) Automatic Speech Intelligibility Detection for Speakers with Speech Impairments: The Identification of Significant Speech Features. Sains Malaysiana, 48 (12). pp. 2737-2747. ISSN 0126-6039, DOI

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Selection of relevant features is important for discriminating speech in detection based ASR system, thus contributing to the improved performance of the detector. In the context of speech impairments, speech errors can be discriminated from regular speech by adopting the appropriate discriminative speech features with high discriminative ability between the impaired and the control group. However, identification of suitable discriminative speech features for error detection in impaired speech was not well investigated in the literature. Characteristics of impaired speech are grossly different from regular speech, thus making the existing speech features to be less effective in recognizing the impaired speech. To overcome this gap, the speech features of impaired speech based on the prosody, pronunciation and voice quality are analyzed for identifying the significant speech features which are related to the intelligibility deficits. In this research, we investigate the relations of speech impairments due to cerebral palsy, and hearing impairment with the prosody, pronunciation, and voice quality. Later, we identify the relationship of the speech features with the speech intelligibility classification and the significant speech features in improving the discriminative ability of an automatic speech intelligibility detection system. The findings showed that prosody, pronunciation and voice quality features are statistically significant speech features for improving the detection ability of impaired speeches. Voice quality is identified as the best speech features with more discriminative power in detecting speech intelligibility of impaired speech. © 2019 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.

Item Type: Article
Funders: Ministry of Higher Education, Malaysia under UM High Impact Research Grant UM-MOHE UM.C/HIR/MOHE/FCSIT/05, Universiti Kebangsaan Malaysia under Young Researchers Incentive Grant GGPM-2017-020
Uncontrolled Keywords: Automatic speech intelligibility detection; Speech detection; Speech features; Speech impairments
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 03 Feb 2020 02:42
Last Modified: 03 Feb 2020 02:42

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