| Autor: |
H M, Chandrashekar, Karjigi, Veena, Sreedevi, N |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Neural Systems & Rehabilitation Engineering; Dec2020, Vol. 28 Issue 12, p2880-2889, 10p |
| Abstrakt: |
Speech disorders linked to neurological problems affect person’s ability to communicate through speech. Dysarthria is one of the speech disorders caused due to muscle weakness producing slow, slurred and less intelligible speech. Automatic intelligibility assessment of dysarthria from speech can be used as a promising clinical tool in treatment. This paper explores the use of perceptually enhanced Fourier transform spectrograms and Constant-Q transform spectrograms with CNN to assess word level and sentence level intelligibility of dysarthric speech from UA and TORGO databases. Constant-Q transform and perceptually enhanced mel warped STFT spectrograms performed better in the classification task. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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