Name
Abstract | ||
|---|---|---|
•A two-stage framework is proposed for the automatic assessment of intelligibility of dysarthric speech using glottal features.•In the first stage, two-class severity classification of dysarthria is performed using support vector machines (SVMs).•In the second stagem, intelligibility estimation of dysarthric speech is computed using a linear regression model.•Two sets of glottal parameters are explored: (1) time-domain and frequency-domain parameters and (2) parameters based on principal component analysis (PCA).•Evaluation results show improved intelligibility assessment measures (correlation and root mean square error) for the glottal parameters compared to the baseline features. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.specom.2020.06.003 | Speech Communication |
Keywords | DocType | Volume |
Dysarthric speech,Intelligibility assessment,Glottal parameters | Journal | 123 |
ISSN | Citations | PageRank |
0167-6393 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. P. Narendra | 1 | 59 | 8.32 |
| Paavo Alku | 2 | 728 | 98.07 |