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Factor Analysis of MRI-Derived Tongue Shapes Mark Hasegawa-Johnson ECE Department and Beckman Institute University of Illinois at Urbana-Champaign Background The vowel sounds of English are classified in two dimensions: “high/low” and “front/back.” u High i e o ae a Low Front Back

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Factor Analysis of MRI-Derived Tongue Shapes

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Factor analysis of mri derived tongue shapes l.jpg

Factor Analysis of MRI-Derived Tongue Shapes

Mark Hasegawa-Johnson

ECE Department and Beckman Institute

University of Illinois at Urbana-Champaign


Background l.jpg

Background

The vowel sounds of English are classified in two dimensions: “high/low” and “front/back.”

u

High

i

e

o

ae

a

Low

Front

Back


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Background

Tongue is composed of about 9 muscles (4 intrinsic, 5 extrinsic)

Superior

Longitudinalis

Palatoglossus

Styloglossus

Verticalis

Superior Phar.

Constrictor

Transversus

Genioglossus

Inferior

Longitudinalis

Hyoglossus


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Theories of Motor Control

Theory 2:

Hierarchical

Control

Theory 1:

Direct

Control


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Factor Analysis of X-Ray ImagesHarshman, Ladefoged, &Goldstein, 1977


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Factor Analysis of X-Ray ImagesHarshman, Ladefoged, &Goldstein, 1977


Factor analysis of x ray images harshman ladefoged goldstein 19777 l.jpg

Factor Analysis of X-Ray ImagesHarshman, Ladefoged, &Goldstein, 1977


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Factor Analysis of X-Ray ImagesHarshman, Ladefoged, &Goldstein, 1977

Finding: Two factors account for 92% of variance.


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Factor loadings

seem to represent

distinctive

features:

v1 = [a front]

v2 = [b high]


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Can Three-Dimensional TongueShape be Explained Using ShapeFactors?

Hypothesis 1

3D tongue shape during speech = weighted sum of 2-3 factors.

Hypothesis 2

Shape of the factors t1(i), t2(i) is speaker-dependent. (??)


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Why is 3D Different from 2D?

Linear Source-Filter Theory:

- Vowel Quality is Determined by Areas

- Area Correlated w/Midsagittal Width


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Do Shape Factors Exist in 3D?

  • If inter-speaker shape similarity is governed by desire for acoustic similarity, and...

  • If acoustic similarity depends on cross-sectional area, not cross-sectional shape...

  • Then

    Variation in 3D Shape May Not Have

    a Shape Factor Basis


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Factor Analysis of MRI-Derived Tongue Shapes: Methodology

1. Recruit Subjects

2. Collect MRI Images

3. Segment the Images

4. Interpolate ROI to Create 3D Tongue Shapes for Each Vowel

5. Speaker-Dependent Factor Analysis

6. Speaker-Independent Factor Analysis


Subject recruitment l.jpg

Subject Recruitment:

  • Ten subjects recruited; five successfully imaged (3 male, 2 female).

  • Subjects were college undergrads and grads with no metal fillings and no claustrophobia.

  • Subjects were trained to sustain vowel sounds with little variation.

  • Human subjects approval: both UCLA and Cedars-Sinai Medical Center.


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MRI Image Collection

  • GE Signa 1.5T

  • T1-weighted

  • 3mm slices

  • 24 cm FOV

  • 256 x 256 pixels

  • Coronal, Axial

  • 11-18 Sounds

  • per Subject.

  • Breath-hold in

  • vowel position

  • for 25 seconds


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Image Viewing and Segmentation: the CTMRedit GUI and toolbox

  • Display series of CT or MR image slices

  • Segment ROI manually or automatically

  • Interpolate and reconstruct ROI in 3D space


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Calibration: Segmentation of Phantom (J. Cha)

  • Test tubes of 3 sizes

  • Radius estimated from manual segmentation has an absolute error of

    • typical case: 0.1mm

    • worst case: 0.4mm


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Calibration: Articulatory Speech Synthesis (J. Cha)

  • /a,i,u/ synthesized using Maeda articulatory synthesizer

  • F1-F4 errors:

    • worst case: +/- 30%

    • mean error: +2.8%

    • std dev: 19.5%


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Reconstruction of ROI

  • Interpolate between image slices to create 3D object.


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Tongue Shape During /ae/


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Speaker Normalization: VT Length,

Inter-Molar Width (S. Pizza)


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Speaker-Dependent Factor Analysis

  • 12 tongue shapes from one speaker:

    • Each tongue shape modeled as a 25 point x 40 point rubber sheet.

  • Principal Components Analysis:

    • 11 Non-Zero Factors (12 vowels - 1 mean vector = 11 degrees of freedom).

    • 2 Factors: 78% of variance

    • 3 Factors: 88% of variance


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“Excuses:” Why Didn’t it Work?

  • Tongue Length changes from /ao/ to /iy/.

  • Human Transcriber Error?

  • Interpolation to Form 3D Image Causes Error

    • Spline & Sinc interpolation: very large errors

    • Linear interpolation: smaller errors, but still too large.


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New Approaches: ---- Avoid Interpolation

General Method: Avoid interpolation by modeling the measured data directly.

  • J. Huang: Control factor shape using an a priori probability distribution.

  • Y. Zheng: Limit factor to the set of polynomial surfaces.


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Polynomial Smoothing (Y. Zheng)

  • Polynomial Surface Modeling

    • Tongue shape = polynomial surface

    • 4D surface model enforces smoothness constraints.

  • Hybrid Polynomial/Factor model

    • Midsagittal tongue shape is as predicted by Harshman et al.

    • 3D shape = (midsag. shape)X(polynomial)


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Conclusions

  • X-ray analysis suggests hierarchical motor control, but...

  • “Hierarchical control” might reflect structure of the acoustic space.

  • MRI analysis does not find hierarchical control (yet), but...

  • Negative finding might be result of methodological weakness.


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Speaker-Dependent Factor Analysis


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