Transcript Coarticulation Analysis of Dysarthric Speech

Coarticulation Analysis of
Dysarthric Speech
Xiaochuan Niu,
advised by Jan van Santen
Outline
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Goal of the Dysarthria Project
Problems
Hypotheses
Analysis approach
Results
Conclusions
Goal of the Dysarthria Project
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Dysarthria: Motor speech impairment
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Dysarthric speech:
Normal speech:
Improve the intelligibility of Dysarthric
speech:
Dysarthric
Speech
Speech
Transformation
System
Intelligible
Speech
Problems
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Previous results of intelligibility test (by
Hosom, Kain, et al.):
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Improvement potential:
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Baseline transformation system:
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Dysarthric: 68% --> Normal: 99%
Spectral feature replacement: 87%
GMM + Linear transformation
NO improvement: 67%
Poor spectral separability of dysarthric
speech
Hypotheses
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Poor spectral separability caused by
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H1 - Target shift
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H2 – Coarticulation effect
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Dysarthric speakers develop special vocal-tract
configurations for certain phonemes.
Degree of context influence on articulation is
greater in dysarthric speech
H3 - Random variation
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Dysarthric speakers can not repeat the target
vocal-tract configuration accurately
Analysis approach
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Acoustic measure
Speech data
Coarticulation model
Estimation method
Acoustic measure
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Formants
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Natural frequencies of certain vocal-tract
configurations
Assumption: each phoneme has a target
formant pattern
Formant trajectories
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Dynamic characteristics of articulation
Acoustic measure (example)
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/b/
Formant trajectories of CVC’ segments
/i:/
/t/
F3
F2
F1
/b/
/u/
/t/
Speech data
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Utterance
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Phoneme
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One dysarthric speaker and one normal speaker
from a dysarthric database
74 nonsense sentences per speaker
Manually labeled with time alignments
Formants
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First three formant frequencies at the midpoints of
vowels in CVC’ segments
Automatically extracted and manually checked
Coarticulation model

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F (t)   (t)  TC  TV  TV   (t)  TC  TV
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  (t) TC  1 (t)   (t)TV   (t) TC
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TV
F (t )
TC
Notations:
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Observed formant vectors: F (t )
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Target formant vectors: TC , TV , and TC
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Coarticulatory factors:
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 (t ) and  (t )
TC
Estimation method
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 (i )
Given N samples of observed formant vectors F at
midpoints of vowels in CVC’ segments, assume target
formant-vectors are known,
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 
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 (i )  (i )
 (i )  (i )  (i )  (i )  (i ) 
F  TV  TC  TV
TC  TV
 (i ) 
 
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(i  1 ~ N ).
(i )
(i )

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With
and
fixed, jointly estimate target
formant-vectors from equations
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 (i )
F   (i )  I 1   (i )   (i )
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 (i )
TC 
1 0 0
  (i ) 
(i )
 I   I TV  (i  1 ~ N ) , I  0 1 0.
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T  (i ) 
0 0 1
C 
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Results – H1
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Vowel space (/i:, @, A, u/): dysarthric ~ normal
Results – H2
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Coarticulation effects: 1     
Conclusions
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An approach to decompose the contributions
of three factors
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Practical aspects of the approach
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target shift / coarticulatory effect / random
variation
Initial targets
Target constraints in estimation
Future work
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Analysis of the entire trajectory
Apply results in the transformation system