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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

Factor Analysis of MRI-Derived Tongue Shapes

Mark Hasegawa-Johnson

ECE Department and Beckman Institute

University of Illinois at Urbana-Champaign


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












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






Superior Phar.







theories of motor control
Theories of Motor Control

Theory 2:



Theory 1:



factor analysis of x ray images harshman ladefoged goldstein 19778
Factor Analysis of X-Ray ImagesHarshman, Ladefoged, &Goldstein, 1977

Finding: Two factors account for 92% of variance.


Factor loadings

seem to represent



v1 = [a front]

v2 = [b high]

can three dimensional tongue shape be explained using shape factors
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. (??)

why is 3d different from 2d
Why is 3D Different from 2D?

Linear Source-Filter Theory:

- Vowel Quality is Determined by Areas

- Area Correlated w/Midsagittal Width

do shape factors exist in 3d
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

factor analysis of mri derived tongue shapes methodology
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
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.
mri image collection
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
image viewing and segmentation the ctmredit gui and toolbox
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
calibration segmentation of phantom j cha
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
calibration articulatory speech synthesis j cha
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%
reconstruction of roi
Reconstruction of ROI
  • Interpolate between image slices to create 3D object.

Speaker Normalization: VT Length,

Inter-Molar Width (S. Pizza)

speaker dependent factor analysis
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
excuses why didn t it work
“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.
new approaches avoid interpolation
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.
polynomial smoothing y zheng
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)
  • 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.