Duration of the rhotic approximant /ɹ/ in spastic dysarthria of different severity levels
| Autor: | Paavo Alku, N. P. Narendra, Anil Kumar Vuppala, Krishna Gurugubelli |
|---|---|
| Přispěvatelé: | International Institute of Information Technology Hyderabad, Speech Communication Technology, Dept Signal Process and Acoust, Aalto-yliopisto, Aalto University |
| Rok vydání: | 2020 |
| Předmět: |
Linguistics and Language
medicine.medical_specialty Context (language use) 02 engineering and technology Audiology 01 natural sciences Language and Linguistics Dysarthria 0103 physical sciences Motor speech disorders otorhinolaryngologic diseases 0202 electrical engineering electronic engineering information engineering medicine Articulatory gestures 010301 acoustics business.industry Communication Spastic dysarthria Rhotic approximant 020206 networking & telecommunications medicine.disease nervous system diseases Computer Science Applications Formant Duration (music) Modeling and Simulation Computer Vision and Pattern Recognition medicine.symptom business Articulation (phonetics) Quasi-closed-phase analysis Software |
| Zdroj: | Speech Communication. 125:61-68 |
| ISSN: | 0167-6393 |
| Popis: | Dysarthria is a motor speech disorder leading to imprecise articulation of speech. Acoustic analysis capable of detecting and assessing articulation errors is useful in dysarthria diagnosis and therapy. Since speakers with dysarthria experience difficulty in producing rhotics due to complex articulatory gestures of these sounds, the hypothesis of the present study is that duration of the rhotic approximant /ô/ distinguishes dysarthric speech of different severity levels. Duration measurements were conducted using the third formant (F3) trajectories estimated from quasi-closed-phase (QCP) spectrograms. Results indicate that the severity level of spastic dysarthria has a significant effect on duration of /ô/. In addition, the phonetic context has a significant effect on duration of /ô/, the I-r-E context showing the largest difference in /ô/ duration between dysarthric speech of the highest severity levels and healthy speech. The results of this preliminary study can be used in the future to develop signal processing and machine learning methods to automatically predict the severity level of spastic dysarthria from speech signals. |
| Databáze: | OpenAIRE |
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