adaptation of orofacial clones to the morphology and control strategies
DESCRIPTION
Adaptation of orofacial clones to the morphology and control strategies of target speakers for speech articulation. Julián Andrés VALDÉS VARGAS Jury: Michel DESVIGNES (President) Yves LAPRIE (Reviewer) Rudolph SOCK (Reviewer) Thierry LEGOU (Examiner) Pierre BADIN (Thesis Director). 1. - PowerPoint PPT PresentationTRANSCRIPT
Adaptation of orofacial clones to the morphology and control strategies of target speakers for speech articulation
Julián Andrés VALDÉS VARGAS
Jury:
Michel DESVIGNES (President)
Yves LAPRIE (Reviewer)
Rudolph SOCK (Reviewer)
Thierry LEGOU (Examiner)
Pierre BADIN (Thesis Director)
1
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• Context of visual articulatory feedback
• Articulatory data
• Individual models and characterisation
• Multi-speaker models
• Conclusions and perspectives
2
Summary
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• Context of visual articulatory feedback
• Articulatory data
• Individual models and characterisation
• Multi-speaker models
• Conclusions and perspectives
3
Summary
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Context• Mastery of articulators for speech production
• Skill maintained/improved by Perception-action loop (Matthies et al., 1996)
• Feedback in speech– Auditory
– proprioceptive
4
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Vision of articulators• Augmented speech Visual feedback
– Display of articulators
• Vision of lips and face– Improves speech intelligibility (Sumby and Pollack, 1954)
– Speech imitation is faster (Fowler et al., 2003)
• Vision of hidden articulations– Increases intelligibility (Badin et al.,2010)
5
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Visual articulatory feedback system• System of visual articulatory feedback (Ben Youssef et al.,
2011)
• Applications– Speech rehabilitation– Computer Aided Pronunciation Training (CAPT)
6
Speech sound
signal of a given
speaker
Visual articulatory feedback system
Clone’s animation
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Problem of articulatory adaptation• Animation of clone based on a single speaker
• Adaptation to several speakers
7
Speech sound
speaker 1
Visual articulatory feedback system
Speech sound
speaker 2
Speech sound
speaker n
Animation based onreference speakerMismatch between
clone’s animation and real speakers
Acoustic Adaptation
(Atef BEN YOUSSEF) Articulatory
adaptation
Animation based onentry speaker
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• Morphology – Different vocal tracts
• Size, vertical / horizontal lengths ratios• Shape (e.g. concave / flat palates)
• Articulatory control strategies– Cope with morphology different articulatory strategies to achieve sounds
considered equivalent for speech communication purposes
8
Inter-speaker variability
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Illustration of speaker differences
/a/
/i/
/u/
Speaker PB Speaker AA Speaker YL
9
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Objectives• Articulatory adaptation (Initial objective)
normalization: extraction of common components (patterns) to control the articulators of several speakers.
• To acquire knowledge about inter-speaker variability
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• Context of visual articulatory feedback
• Articulatory data
• Individual models and characterisation
• Multi-speaker models
• Conclusions and perspectives
11
Summary
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Articulatory data• Type of data Articulatory data Building
articulatory models• Inter-speaker variability:
• 11 French speakers (6 males and 5 females)
• Articulatory phonetic coverage: • 13 vowels• 10 consonants in 5 vocalic contexts
(vowel-consonant-vowel) • 63 articulations in total
12
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Recording Methods• Several recording methods considered:
• X-ray (Meyer (1907) ,Mosher (1927))
• Difficult to accurately identify the contours
• Electro-Magnetic Articulography (EMA)• No recording of the whole vocal tract
• Magnetic Resonance Imaging (MRI)
(Rokkaku et al., 1986)
• Tomographic (imaging by sections)
• Maintained vocal tract positions
• Speakers in supine position Gravitational effect is moderate
(Engwall (2003; 2006) )
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Decision to use MRI • Whole vocal tract information ≠ EMA
• Contours easier to identify compared to X-ray
• No health hazard compared to X-ray
• Recording parameters:• Midsagittal image of the vocal tract
• Slice thickness: 4 mm
• Spatial resolution: 1 mm / pixel
• Acquisition time: 8 -16 seconds
14
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MRI Recording• The speaker is asked to go through several stages
• Speakers lay in supine position
• Bed shifted into the MRI machine
• Setting up of alignment recording properties
• Maintained pronunciation of articulations for 8-16 seconds.
• Speakers are asked not to move
their heads
15
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Processing of MRI• Midsagittal contours manually edited
16
• Rigid contours are drawn once for a given speaker• Positioning of palate using skull bones as reference• Rotation and translation
• Positioning of jaw by means of rototranslations• Edition of deformable contours: Lips, tongue, velum, etc.• Palate of all articulations are aligned• Avoidance of noise introduced by head moving
/a/ /i/ /u/
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Contours modelled• Upper tongue: 150 (x,y) points• Lips: 100 (x,y) points• Velum: 150 (x,y) points
17
• Static data Articulatory study/models
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• Context of visual articulatory feedback
• Articulatory data
• Individual models and characterisation
• Multi-speaker models
• Conclusions and perspectives
18
Summary
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Universal control parameters• Extraction of common set of patterns (components)
• Goals:– Building individual-speaker articulatory models
– Controlling all individual articulatory models from a universal set of components
19
UniversalSet of
Components
Speaker 1
Speaker 2
/a//i//u/
/a//i//u/
/a//i//u/
/a//i//u/
Articulator contours of individual
speakers
Universal model
Universal model
Speaker specificweights
Speaker specificweights
CP/a/CP /i/CP/u/
CP/a/CP /i/CP/u/
CP/a/CP/i/CP/u/
CP/a/CP/i/CP/u/
Components
Mspeaker1Mspeaker1Speaker 1
Speaker 2 Mspeaker2Mspeaker2
/a//i//u/
/a//i//u/
/a//i//u/
/a//i//u/
Articulator contours of individual
speakers
CP/a/CP/i/CP/u/
CP/a/CP/i/CP/u/
Individual articulatory
models
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Method for individual models of speakers
• Principal component analysis (PCA)• dimensionality reduction extraction of orthogonal components
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• Evaluation of model for a individual speaker X• Variance explanation
• Root Mean Square Error (RMSE)
21
Assessment of models
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• Performance of models to reconstruct data that was not used for training
• Leave-one-out cross validation procedure (a.k.a. Jackknife)
• Observation left out Reconstruction of observation left out by inverting the model
Validation of generalization properties
Valuable predictors retained
22
Generalization properties of models
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• Guided PCA model (Badin & Serrurier (2006))
• 4 components extracted
23
Individual tongue models• First component extracted by Linear regression
• Jaw Height (predictor)
Three degrees of freedom: x,y translation and rotation (Edwards & Harris, 1990)
Normalized value of the y-coordinate of the lower incisor (Badin & Serrurier (2006))
(X,Y)
Corr(Y, θ) ≈ 0.92
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Individual tongue models• Other 3 components extracted by PCA from the
residue:• Tongue Body (TB)
• Tongue Dorsum (TD)
• Tongue Tip (TT)
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Individual tongue models• Other 3 components extracted by PCA from the
residue:• Tongue Body (TB)
• Tongue Dorsum (TD)
• Tongue Tip (TT)
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Individual tongue models• Other 3 components extracted by PCA from the
residue:• Tongue Body (TB)
• Tongue Dorsum (TD)
• Tongue Tip (TT)
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Speakers
Per
cent
age
of v
aria
nce
expl
aine
d by
eac
h co
mpo
nent
%
JH
PB YL LH RL LD BR HL AA MG AK MGO0
5
10
15
20
25
30
JH 4.23%Subject AK TB 36.68%
TD 23.39% TT 16.92%
JH 4.31%Subject RL TB 41.41%
TD 22.36% TT 13.70%
JH 26.11%Subject LD TB 29.40%
TD 20.02% TT 12.06%
27
Comparison between components
• JH component:• Max. variance: LD• Min. variance: RL, MG, AK• Compensation strategy of MG
0 50 100 150-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6comparison of slopes
BACK
Co
eff
icie
nts
of
LR
: J
H v
s. t
on
gu
e v
ert
ex
TONGUE TIP
RL
LDMG
AK
• TB component:• Represents more variance
than other components• Horizontal/diagonal back-front
movement
JH 25.28%Subject LD TB 31.47%
TD 20.07% TT 10.79%
JH 4.31%Subject RL TB 41.41%
TD 22.36% TT 13.70%
JH 6.21%Subject AK TB 35.92%
TD 22.73% TT 16.65%
Speaker LD Speaker RL Speaker AK
JH 25.28%Subject LD TB 31.47%
TD 20.07% TT 10.79%
JH 4.31%Subject RL TB 41.41%
TD 22.36% TT 13.70%
JH 6.21%Subject AK TB 35.92%
TD 22.73% TT 16.65%
• TD component:• vertical/diagonal arching
movement
• TT component:• Used in different proportion
according to the speaker
JH 25.28%Subject LD TB 31.47%
TD 20.07% TT 10.79%
JH 4.31%Subject RL TB 41.41%
TD 22.36% TT 13.70%
JH 6.21%Subject AK TB 35.92%
TD 22.73% TT 16.65%
Y-Tongue = Coefficients_LR * JH• Nomograms: graphical representation of components
• Variation between -3 to 3
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3 4 5 6 7
7
8
9
10
11
12
13var(UL): 1.68% - var(LL): 30.90%
JHJHJHJHJHJHJHJHJHJHJHJHJH
7
8
9
10
11
12
13var(ULP): 21.93% - var(LLP): 34.85%
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
var(ULH): 55.03% - var(LLH): 20.51%
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
Speaker RL
Individual lips models
3 4 5 6 7
7
8
9
10
11
12
13var(UL): 25.19% - var(LL): 44.59%
JHJHJHJHJHJHJHJHJHJHJHJHJH
7
8
9
10
11
12
13var(ULP): 52.74% - var(LLP): 28.64%
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
ULP
LLP
var(ULH): 12.75% - var(LLH): 15.36%
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
ULH
LLH
Speaker LD• 3 components extracted by
Guided PCA model (Badin et al., 2012)
• Jaw Height• More influence on LL than UL
• Little influence on UL for RL
• Protrusion• ULP > LLP for speaker LD
• LLP > ULP for speaker RL
• Lip height• ULH > LLH for all speakers
Except for speaker LD
25.2%
44.6%
52.7%
28.6%
12.7%
15.4%
1.7%
31%
21.9%
34.8%
55%
20.5%
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• 2 components extracted by PCA (Serrurier & Badin, 2008):
• Velum levator (Oblique movement) - VL
• Superior pharyngeal constrictor (horizontal movement) - VS
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Individual velum models
4 6 8 10 12 144
5
6
7
8
9
10
11
12
13
14
15var(PCA-1): 77.67 %
PCA-1
Speaker AA
4
5
6
7
8
9
10
11
12
13
14
15var(PCA-2): 17.32 %
PCA-2
VL VS
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Individual velum models: consonant /ʁ/
Speaker AA Speaker HL
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2-8
-6
-4
-2
0
2
4
kEka ke kiku
tEta
te titu
E Oaan eX oon
xuy i
RERaRe
Ri
Ru
SESa
Se
SiSufEfafefi fulE
lalelilumE
ma memi
mu
nEna neni
nu
pE papepi
pu
sE sasesisu
PCA-1 vs PCA-2 for speaker aa
PCA-1
PC
A-2
-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2-8
-6
-4
-2
0
2
4
kE
ka
ke kiku tE
ta
te
tituE
O
aane
X
o
on
x
u
yi
RE
RaReRi
RuSE
Sa
Se
SiSufEfa
fefifulEla
leli
lu
mE
mame
mimunE
na
ne
ninu
pE
pa
pepi pu
sE
sa
se
si
su
PCA-1 vs PCA-2 for speaker hl
PCA-1
PC
A-2
/ʁa/
VL VL
VS VS
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Conclusions: individual models• Tongue PCA models: 4 components
(JH,TB,TD,TT)• Variance Explained: 93%, RMSE: 0.13 cm
• Lip models: 3 components (JH, Protrusion, Height)• Variance Explained: 94%, RMSE: 0.04 cm
• Velum models: 2 components (VL, VS)• Variance Explained: 90%, RMSE: 0.08 cm
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• Context of visual articulatory feedback
• Articulatory data
• Individual models and characterisation
• Multi-speaker models
• Conclusions and perspectives
32
Summary
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Literature on multi-speaker models
• PARAFAC models : 2 components extracted
• Studies based on EMA (Hoole(1998), Geng(2000), Hu(2006))• 6-7 speakers, 10-15 vowels, 3-4 sensors on the
tongue, 80%-96% variance explained.
• Study based on X-ray: Harshman(1977)
• 5 speakers, 10 vowels, 13 points, 92.7%
• Studies based on MRI (Hoole(2000), Zheng(2003), Ananth(2010))
• 3-9 speakers, 7-13 vowels, 13-150 points, 71%-87% of variance exp.
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Multi-speaker decomposition methods Extraction of common set of components PARAFAC (Harshman,1970) (three-way factor
analysis, diagonal speaker adaptation matrix)
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TUCKER 3 Extension of PARAFAC Decomposition in all modes of variation
35
Multi-speaker decomposition methods
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Joint PCA (two-way analysis adapted to multi-speaker models) (Ananthakrishnan et al. (2010) – KTH(Sweden))
All speakers articulatory measurements for one phoneme considered as one set of data
forces common components
36
Multi-speaker decomposition methods
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• RMSE and Variance Explained (VarEx)• multi-speaker model (red, green, black) vs.
• average of individual speakers’ models (blue)
VarEx RMSE
2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number Of Components
Per
cent
age
Var
ianc
e E
xpla
ined
Average Variance explained of methods
Average PCA
Joint PCA
PARAFAC
TUCKER
2 4 6 8 10 12 14 16 18 200
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Number Of Components
RM
S E
rror
in c
m
Average Rmse of methods
Average PCA
Joint PCAPARAFAC
TUCKER
Comparison of performance between methods
37
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• Reference PCA model with 4 components
• Total number of components: 11 x 4 = 44
• Student's t-test for RMSE at 5% signif. level
• Joint PCA: 14 – 21 components ( TUCKER )
• PARAFAC: 21 components
VarEx RMSE
2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Number Of Components
Per
cent
age
Var
ianc
e E
xpla
ined
Average Variance explained of methods
Average PCA
Joint PCA
PARAFAC
TUCKER
2 4 6 8 10 12 14 16 18 200
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Number Of Components
RM
S E
rror
in c
m
Average Rmse of methods
Average PCA
Joint PCAPARAFAC
TUCKER
Multi-speaker Tongue models
38
• Student's t-test -> determine if the RMSE of models are significantly different from each other
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• Individual models:
• Reference PCA model with 44 (11 x 4) components• VarEx: 93.23 %
• RMSE: 0.13 cm
• Multi-speaker models:
• Joint PCA with 4 components• VarEx: 72.16 %
• RMSE: 0.27 cm
• Interpretation of components: JH, TB, TD and TT
• Equivalent solution: Joint PCA, 21 components• VarEx: 94.88%
• RMSE: 0.12 cm
• Lack of interpretation from the 5th component
Literature
No. Components: 2VarExp: 71% - 96%Corpus: 7-15 vowelsSpeakers: 3-9
Present studyCorpus: 63 articulations (vowels and consonants)Speakers: 11 speakers
Multi-speaker Tongue models
39
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Multi-speaker modelslips and velum• Lips and velum models comparable with tongue models
• Lips
individual models: 33 components (3 * 11)
multi-speaker joint PCA models: equivalent with 21 components
Reduced no. of components: 3 interpretable components
(JH, protrusion, lip height)
• Velum
individual models: 22 components (2 * 11)
multi-speaker joint PCA models: equivalent with 14 components
Reduced no. of components: 2 components
(Oblique, horizontal)
40
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• Context of visual articulatory feedback
• Articulatory data
• Individual models and characterisation
• Multi-speaker models
• Conclusions and perspectives
41
Summary
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Conclusions Data
Unique set of articulatory data for French MRI for the whole vocal tract for 11 French speakers Contours Vowels and consonants More speakers compared to the literature
Characterisation of different speakers’ strategies Tongue Upper and lower lip Velum
Multi-speaker models (normalisation) of tongue, lips and velum contours
No work in the literature on lips and velum
42
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Perspectives
43
More speakers Relation between articulatory strategies and acoustics Cross-speaker velum variability
Influence of the tongue movement Nasality
new modelling solutions Non-linear methods:
Kernel PCA Artificial Neural Networks (ANN) Support Vector Machines (SVM)
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Acknowledgments Laurent Lamalle (IRMaGe, Grenoble) Speakers ARTIS project (GIPSA-lab, LORIA)
43
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Thank you for your attention
Questions?
44
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• Maeda S. (1979) Fix grid
• Busset J.(2013) : Adaptive grid system Euclidean coordinates (intersections) Distances and extreme angles Polar coordinates (distances and angles for each grid line)
• Beautemps et al. (2001): adapted to each articulation
Euclidean coordinates
Distances and TngAdv + TngBot
46
Grid system
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PB = 0.6611
YL = 0.7385
LH = 0.7174
RL = 0.3946
LD = 0.8423
BR = 0.7764
HL = 0.7913
AA = 0.4952
MG = 0.4151
AK = 0.8317
MGO = 0.9228
47
Corr(Y-jaw,Angle_rotation)
(X,Y)
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• Grid system
Midsagittal function vocal tract area function (series of areas and lengths of
each sagittal section) α , β models (Beautemps et al.1995; Heinz & Stevens, 1965)
A = Area of a given grid section, d = midsagittal distance
α , β coefficients depending on subject and vocal tract location
α , β according to speaker of reference: PB
vocal tract acoustic transfer function (Fant, 1960; Badin & Fant, 1984)
Formants
48
Acoustic simulation
d A
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No. Coefficients by method
2 4 6 8 10 12 14 16 18 200
0.5
1
1.5
2
2.5
3
3.5
4x 10
5
No. of components
No.
of
coef
ficie
nts
PCA
PARAFACJoint PCA
TUCKER
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“Essentially, all models are wrong, but some are useful“
George Edward Pelham Box
50
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Joint PCA (two-way analysis adapted to multi-speaker models) (Ananthakrishnan et al. (2010) – KTH(Sweden))
All speakers articulatory measurements for one phoneme considered as one set of data
forces common components
51
Multi-speaker decomposition methods
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Generalisation
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• Estimation of non visible landmarks (Tongue tip and jaw attachment)
• Computed as the average position of the articulations in which is distinguishable
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Articulatory data
Not distinguishable tongue tip Not distinguishable jaw attachment
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State of the art on articulatory normalisation
• Articulatory normalisation based on linear decomposition methods• PARAFAC tongue models, 2 components extracted
• Data: 7 – 15 vowels, 3 – 9 speakers
• Performance: 71% - 96% of variance explanation
• Geometric normalisation• Scaling transformations -> do not normalise articulatory control
strategies employed by different speakers
• Challenge• Modelling of other contours such as lips and velum
• Extension to consonants54
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Linear regression between couple of speakers
2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Per
cent
age
varia
nce
expl
aine
d
Number of components
Variance explanation of prediction of speaker pb
PCA model of pbPrediction from yl
Prediction from lh
Prediction from rl
Prediction from ld
Prediction from brPrediction from hl
Prediction from aa
Prediction from mg
Prediction from akPrediction from mgo
2 4 6 8 10 12 14 16 18 200
0.1
0.2
0.3
0.4
0.5
RM
S E
rror
in c
m
Number of components
RMSE of prediction of speaker pb
• Prediction of PCA control parameters of a target speaker (πTS) from PCA control parameters of a source speaker (πSS) Multi-linear Regression
TS SSi
n
cmpi
1
VarEx RMSE
• Overfitted from 10th component on LOOCV
• 10th components 64.32 % variance explained, 0.37 cm (RMSE)
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Speakers
Per
cent
age
of v
aria
nce
expl
aine
d by
eac
h co
mpo
nent
%
JH
TB
TD
TT
PB YL LH RL LD BR HL AA MG AK MGO0
10
20
30
40
50
60
70
80
90
56
Individual tongue modelsJH 25.28%Subject LD TB 31.47%
TD 20.07% TT 10.79%
JH 4.21%Subject RL TB 22.84%
TD 41.23% TT 13.50%
JH 6.21%Subject AK TB 35.92%
TD 22.73% TT 16.65%
1 1 1
1
1
Individual tongue models: Synergy jaw-tongue
Max Min ~= speakers RL, MG,AK
0 50 100 150-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6comparison of slopes
BACK
Co
eff
icie
nts
of
LR
: J
H v
s. t
on
gu
e v
ert
ex
TONGUE TIP
rl
ldmg
ak
Y-coordinate tongue contour
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• Evaluation of model for a individual speaker X• Variance explanation
• Root Mean Square Error (RMSE)
Xp = speaker data predicted, n = number of observations , m = number of articulator measurements
57
mn
n miX
iX
XVARIANCE.
2)1 1 ()(
)(
)(),(_
XVARIANCEp
XVARIANCE
pXXEXPLAINEDVARIANCE
mn
n mpredictedi
XiX
RMSE.
2)1 1 _(
Assessment of models
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Multi-speaker modelslips and velum• Lips and velum models comparable with tongue
models• Lips
individual models: 33 components (3 * 11)
multi-speaker joint PCA models: 21 components
Reduced no. of components: 3 interpretable components
• Velum
individual models: 22 components (2 * 11)
multi-speaker joint PCA models: 14 components
Reduced no. of components: 2 components
58
Contour
Average PCA Joint PCA according to Student's t-test Joint PCA with reduced no. of components
No. Components
Variance Exp.
RMSENo.
ComponentsVariance Exp. RMSE
No. Components
Variance Exp. RMSE
Upper tongue 44 (4 *11) 93.23% 0.13 cm 21 94.88% 0.12 cm 4 72.16% 0.27 cm
Upper lip 33 (3*11) 94.89% 0.03 cm 21 96.67% 0.03 cm 3 74.28% 0.08 cmLower lip 33 (3*11) 94.50% 0.05 cm 21 96.85% 0.04 cm 3 69.26% 0.15 cm
Velum 22(2*11) 90% 0.08 cm 14 94.20% 0.07 cm 2 76.01% 0.14 cm