2. Coarticulation Analysis of Dysarthric Speech Xiaochuan Niu, advised by Jan van Santen

3. Outline Goal of the Dysarthria Project Problems Hypotheses Analysis approach Results Conclusions

4. Goal of the Dysarthria Project Dysarthria: Motor speech impairment Dysarthric speech: Normal speech: Improve the intelligibility of Dysarthric speech: Speech Transformation System Dysarthric Speech Intelligible Speech

5. Problems Previous results of intelligibility test (by Hosom, Kain, et al.): Improvement potential: Dysarthric: 68% --> Normal: 99% Spectral feature replacement: 87% Baseline transformation system: GMM + Linear transformation NO improvement: 67% Poor spectral separability of dysarthric speech

6. Hypotheses Poor spectral separability caused by H1 - Target shift Dysarthric speakers develop special vocal-tract configurations for certain phonemes. H2 – Coarticulation effect Degree of context influence on articulation is greater in dysarthric speech H3 - Random variation Dysarthric speakers can not repeat the target vocal-tract configuration accurately

7. Analysis approach Acoustic measure Speech data Coarticulation model Estimation method

8. Acoustic measure Formants Natural frequencies of certain vocal-tract configurations Assumption: each phoneme has a target formant pattern Formant trajectories Dynamic characteristics of articulation

9. Acoustic measure (example) Formant trajectories of CVC’ segments F1 F2 F3 /b//i://t//b//u//t/

10. Speech data Utterance One dysarthric speaker and one normal speaker from a dysarthric database 74 nonsense sentences per speaker Phoneme Manually labeled with time alignments Formants First three formant frequencies at the midpoints of vowels in CVC’ segments Automatically extracted and manually checked

11. Coarticulation model Notations: Observed formant vectors: Target formant vectors: Coarticulatory factors:

12. Estimation method Given N samples of observed formant vectors at midpoints of vowels in CVC’ segments, assume target formant-vectors are known, With and fixed, jointly estimate target formant-vectors from equations

13. Results – H1 Vowel space (/i:, @, A, u/): dysarthric ~ normal

14. Results – H2 Coarticulation effects:

15. Conclusions An approach to decompose the contributions of three factors target shift / coarticulatory effect / random variation Practical aspects of the approach Initial targets Target constraints in estimation Future work Analysis of the entire trajectory Apply results in the transformation system