Deep Learning-Based Acoustic Feature Representations for Dysarthric Speech Recognition-Reference-Cited by-同舟云学术

Deep Learning-Based Acoustic Feature Representations for Dysarthric Speech Recognition

Published:2023-03-20 Issue:3 Volume:4 Page:
ISSN:2661-8907
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Latha M.^ORCID,Shivakumar M.,Manjula G.,Hemakumar M.,Kumar M. Keerthi

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s42979-022-01623-x.pdf

Reference14 articles.

1. Latha M, Shivakumar M, Manjula R. A study of acoustic characteristics, prosodic and distinctive features of dysarthric speech. Grenze Int J Comput Theory Eng (Spec Issue). 2018;2018:228–35.

2. Farhadipour A, Veisi H, Asgari M, Keyvanrad MA. Dysarthric speaker identification with different degrees of dysarthria severity using deep belief networks. ETRI J. 2018;40(5):643–52.

3. Mohammed SY, Ahmed SS, Brahim ZF, AsmaBouchair B. Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network. EURASIP J Audio Speech Music Process. 2020;1:1–7.

4. Young S, Evermann G, Gales M, Hain T, Kershaw D, Moore G, Odell J, Ollason D, Povey D, Valtchev V, et al. The htk book (for htk version 3.3). Cambridge University Engineering Department, 2005. 2006.

5. Oue S, Marxer R, Rudzicz F. Automatic dysfluency detection in dysarthric speech using deep belief networks. In: Proceedings of SLPAT 2015: 6th workshop on speech and language processing for assistive technologies. 2015. p. 60–4.

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