Improving Speech to Text Alignment Based on Repetition Detection for Dysarthric Speech-Reference-Cited by-同舟云学术

Improving Speech to Text Alignment Based on Repetition Detection for Dysarthric Speech

Published:2020-04-24 Issue:11 Volume:39 Page:5543-5567
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Diwakar G.,Karjigi Veena

Funder

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s00034-020-01419-5.pdf

Reference29 articles.

1. Alignment results [online] available from: https://github.com/ytyeung/IS2015alignment

2. B. Bigi, K. Klessa, L. Georgeton, C. Meunier, A syllable-based analysis of speech temporal organization: a comparison between speaking styles in dysarthric and healthy populations, in INTERSPEECH (2015), pp. 2977–2981

3. S. Chandrakala, N. Rajeswari, Representation learning based speech assistive system for persons with dysarthria. IEEE Trans. Neural Syst. Rehabil. Eng. 25(9), 1510–1517 (2017)

4. CMU-Sphinx: Open Source Toolkit for Speech Recognition. http://cmusphinx.sourceforge.net

5. G. Diwakar, V. Karjigi, Repetition detection in dysarthric speech, in IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai (2017), pp. 1178–1181

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