Dysarthric speaker identification with different degrees of dysarthria severity using deep belief networks

• Main Authors: Aref Farhadipour, Hadi Veisi, Mohammad Asgari, Mohammad Ali Keyvanrad
• Format: Article
• Language: English
• Published: Electronics and Telecommunications Research Institute (ETRI) 2018-07-01
• Series: ETRI Journal
• Subjects: deep belief network; deep neural network; dysarthria; MFCC; speaker identification
• Online Access: https://doi.org/10.4218/etrij.2017-0260
• ISSN: 1225-6463; 2233-7326

Dysarthria is a degenerative disorder of the central nervous system that affects the control of articulation and pitch; therefore, it affects the uniqueness of sound produced by the speaker. Hence, dysarthric speaker recognition is a challenging task. In this paper, a feature‐extraction method based on deep belief networks is presented for the task of identifying a speaker suffering from dysarthria. The effectiveness of the proposed method is demonstrated and compared with well‐known Mel‐frequency cepstral coefficient features. For classification purposes, the use of a multi‐layer perceptron neural network is proposed with two structures. Our evaluations using the universal access speech database produced promising results and outperformed other baseline methods. In addition, speaker identification under both text‐dependent and text‐independent conditions are explored. The highest accuracy achieved using the proposed system is 97.3%.