Kaytetye coronal contrasts without …linguistics.berkeley.edu/.../files/Ultrafest_Slides_13.pdfKaytetye coronal contrasts without edge-detection Susan Lin, Benjamin Davies, and Katherine

Kaytetye coronal contrasts withoutcontours

Susan Lin, Benjamin Davies, and Katherine [email protected]

ARC Centre for Excellence in Cognition and its DisordersMacquarie University

October 6, 2013Ultrafest VIEdinburgh

Kaytetye coronal contrasts withoutedge-detection

Susan Lin, Benjamin Davies, and Katherine [email protected]

ARC Centre for Excellence in Cognition and its DisordersMacquarie University

October 6, 2013Ultrafest VIEdinburgh

IntroductionProcedures

Results

Ultrasound in practiceA place for qualitative observations?Kaytetye coronal contrast: a test case

Why use ultrasound?

Benefits

I Good spacial resolution

I Reasonable temporal resolution (and improving!)

I Portability

I Immediacy of visual information

Consequence

Portable ultrasound machines are being employed in fieldwork anddocumentation, in increasing numbers.

2


Results


Why use ultrasound?

Benefits

I Good spacial resolution

I Reasonable temporal resolution (and improving!)

I Portability

I Immediacy of visual information

Consequence

Portable ultrasound machines are being employed in fieldwork anddocumentation, in increasing numbers.

2


Results


Typical procedures

1. Collect

2. Post-processI Extract relevant framesI Edge-detection of contours

3. Analyze

4. Interpret

Post-processing may be time-consuming

I Infrastructure cost (in time) high for small projects and/orprojects with simple questions

I Processing time may be too high for performing while on site

3


Results


Typical procedures

1. Collect

2. Post-process

I Extract relevant framesI Edge-detection of contours

3. Analyze

4. Interpret




3


Results


Typical procedures

1. Collect


3. Analyze

4. Interpret




3


Results


Typical procedures

1. Collect


3. Analyze

4. Interpret




3


Results


Typical procedures

1. Collect


3. Analyze

4. Interpret




3


Results


Typical procedures

1. Collect


3. Analyze

4. Interpret




3


Results


Typical procedures

1. Collect


3. Analyze

4. Interpret




3


Results


Reducing the barrier to entry

Question

How can we reduce the barrier to entry (whether real orperceived)?

At least two ways:

I Speed up or automate edge-detection

I Develop alternatives to edge-detection

4


Results



Question


At least two ways:



4


Results



Question


At least two ways:



4


Results



Question


At least two ways:



4


Results


An observation (from a colleague)

./images/poster.jpgaNg@n@Na at”@N@

5


Results


Testing ground: Kaytetye

I Arandic language

I Spoken 300km north ofAlice Springs, NT

I Dozens of speakersremaining

6


Results


Kaytetye coronal inventory (phonemic)

dental alveolar retroflex palatal pre-palatal

oral stop t” t ú c jcnasal n” n ï ñ jñ

pre-stopped nasal t”n” tn úï cñjcñ

lateral l” l í L jL

Test question

Does production of alveolar compared to dental stops in Kaytetyeinvolve different motion of the tongue?

7


Results







Test question


7


Results







Test question


7


Results

DataSightedMeasured

Stimuli

I Target stops elicited in/#a a/ or /#a @/ contexts

I 8-12 repetitions of each item

I 7 female native speakers ofKaytetye (all multi-lingual tosome extent)

I Probe held by researcher

8


Results

DataSightedMeasured

Frames extracted

I 1 vocalic frame, from preceding vowel (a)

I 1 consonantal frame, at maximal constriction of targetconsonant

at”@N@

9


Results

DataSightedMeasured

Frames extracted

I 1 vocalic frame, from preceding vowel (a)

I 1 consonantal frame, at maximal constriction of targetconsonant

at”@N@ at”@N@

9


Results

DataSightedMeasured

Post-processing: sighted

at”@N@

1. Color consonantal frame (red)

2. Turn blacks in consonantal frame transparent

3. Combine frames

10


Results

DataSightedMeasured


at”@N@



3. Combine frames

10


Results

DataSightedMeasured


at”@N@



3. Combine frames

10


Results

DataSightedMeasured

Heights sighted

Naive coder instructed to “mentally trisect the white line in thecenter of the image” and rate

I +1 if red line is further from center than white

I 0 if red line is the same distance as the white from center

I -1 if red line is closer to center than white

11


Results

DataSightedMeasured

Heights sighted

+1 FRONT-1 MID0 BACK

Naive coder instructed to “mentally trisect the white line in thecenter of the image” and rate

I +1 if red line is further from center than white

I 0 if red line is the same distance as the white from center

I -1 if red line is closer to center than white

11


Results

DataSightedMeasured

Post-processing: measured

at”@N@

I Edge-detect both vocalic and consnoantal contours viaEdgeTrak (Li et al., 2005)

I Define three radial lines roughly approximating tonguetip/blade (FRONT), body (MID), and back (BACK)

12


Results

DataSightedMeasured

Post-processing: measured

at”@N@

I Edge-detect both vocalic and consnoantal contours viaEdgeTrak (Li et al., 2005)

I Define three radial lines roughly approximating tonguetip/blade (FRONT), body (MID), and back (BACK)

12


Results

DataSightedMeasured

Distances measured

-4 FRONT-21 MID21 BACK

I Distance along radial lines from vocalic (black) to consonantal(red) contours measured (pixels)

I Positive polarity if consonantal contour further from centerthan vocalic

13


Results

Measured vs. sighted resultsMeasured vs. sighted values

Results: measured

Test question


Yes.

●

●

●

●

●

●

●

alveolar dental

−40

−20

020

40

Back

Place

Dis

tanc

e be

twee

n V

and

C (

px)

●●

alveolar dental

−40

−20

020

40

Middle

Place

Dis

tanc

e be

twee

n V

and

C (

px)

●

alveolar dental

−40

−20

020

40

Front

Place

Dis

tanc

e be

twee

n V

and

C (

px)

n.s. * p < 0.05 * p < 0.05

14


Results


Results: measured

Test question

Does production of alveolar compared to dental stops in Kaytetyeinvolve different motion of the tongue? Yes.

●

●

●

●

●

●

●

alveolar dental

−40

−20

020

40

Back

Place

Dis

tanc

e be

twee

n V

and

C (

px)

●●

alveolar dental

−40

−20

020

40

Middle

Place

Dis

tanc

e be

twee

n V

and

C (

px)

●

alveolar dental

−40

−20

020

40

Front

Place

Dis

tanc

e be

twee

n V

and

C (

px)

n.s. * p < 0.05 * p < 0.05

14


Results


Results: sighted

Test question


Yes, but only in MIDregion.

Back

Place

Hei

ght R

espo

nses

alveolar dental

−1

01

0.0

0.2

0.4

0.6

0.8

1.0

Middle

Place

Hei

ght R

espo

nses

alveolar dental

−1

01

0.0

0.2

0.4

0.6

0.8

1.0

Front

PlaceH

eigh

t Res

pons

es

alveolar dental

01

0.0

0.2

0.4

0.6

0.8

1.0

n.s. * p < 0.05 n.s.

15


Results


Results: sighted

Test question

Does production of alveolar compared to dental stops in Kaytetyeinvolve different motion of the tongue? Yes, but only in MIDregion.

Back

Place

Hei

ght R

espo

nses

alveolar dental

−1

01

0.0

0.2

0.4

0.6

0.8

1.0

Middle

Place

Hei

ght R

espo

nses

alveolar dental

−1

01

0.0

0.2

0.4

0.6

0.8

1.0

Front

PlaceH

eigh

t Res

pons

es

alveolar dental

01

0.0

0.2

0.4

0.6

0.8

1.0

n.s. * p < 0.05 n.s.

15


Results


Results vs. results

Measured Means Sighted Means

BA

CK

MID

*

FRONT*

BA

CK

MID

*

FR

ON

T

Alveolar -5.02 -0.25 11.04 -0.11 -0.12 0.99Dental -5.63 -12.79 8.35 0.13 -0.88 0.98

Results comparison

Sighted ratings capable of distinguishing between polarity, but arenot great at expressing variation in magnitude.

16


Results


Results vs. results


BA

CK

MID

*

FRONT*

BA

CK

MID

*

FR

ON

T

Alveolar -5.02 -0.25 11.04 -0.11 -0.12 0.99Dental -5.63 -12.79 8.35 0.13 -0.88 0.98

Results comparison

Sighted ratings capable of distinguishing between polarity

, but arenot great at expressing variation in magnitude.

16


Results


Results vs. results


BA

CK

MID

*

FRONT*

BA

CK

MID

*

FR

ON

T

Alveolar -5.02 -0.25 11.04 -0.11 -0.12 0.99Dental -5.63 -12.79 8.35 0.13 -0.88 0.98

Results comparison

Sighted ratings capable of distinguishing between polarity, but arenot great at expressing variation in magnitude.

16


Results


Values vs. values

●

●

●

●●

●

●

●

●

−1 0 1

−40

−20

020

40

Back

Height Responses

Dis

tanc

e (p

x)

●●

●

−1 0 1−

40−

200

2040

Mid

Height Responses

Dis

tanc

e (p

x)

●

0 1

−40

−20

020

40

Front

Height Responses

Dis

tanc

e (p

x)

−1 < 0 < +1 −1 < 0 < +1 n.s.

Values direct comparison

Sighted rating is a reasonably good predictor for measureddistances.

17


Results


Values vs. values

●

●

●

●●

●

●

●

●

−1 0 1

−40

−20

020

40

Back

Height Responses

Dis

tanc

e (p

x)

●●

●

−1 0 1−

40−

200

2040

Mid

Height Responses

Dis

tanc

e (p

x)

●

0 1

−40

−20

020

40

Front

Height Responses

Dis

tanc

e (p

x)

−1 < 0 < +1 −1 < 0 < +1 n.s.

Values direct comparison

Sighted rating is a reasonably good predictor for measureddistances.

17


Results

UsabilityViabilityAutomation

Summary

Suitable for

I First glances

I Small projects

I Visualization

I Teaching / demonstration?

Not suitable for

I Measuring distances automatically

I Comparisons involving small effects

I Projects with large amounts of data

18


Results


Summary

Suitable for

I First glances

I Small projects

I Visualization

I Teaching / demonstration?

Not suitable for

I Measuring distances automatically

I Comparisons involving small effects

I Projects with large amounts of data

18


Results


Typical procedures (revisited)

Time investment

Where can we save time?

1. Collect

2. Post-processI Extract relevant framesI Edge-detection

3. Analyze

4. Interpret

19


Results


Typical procedures (revisited)

Time investment

Where can we save time?

1. Collect

2. Post-processI Extract relevant framesI Edge-detection

3. Analyze

4. Interpret

19


Results


Qualitative vs. quantitative methods

Edge-detection or alternative: ?

I Measured (quantitative) –time-consuming

I Sighted (qualitative) – canbe automated?

Analysis: quantitative

I Sighted (qualitative) –human component

I Measured (quantitative) –can be largely automated

Automation

If qualitative methods are to be competitive, automation mustbe made possible.

20


Results









Automation


20


Results









Automation


20


Results









Automation


20


Results


Automation: first (naive) attempt

1. Increase contrast of both frames

2. Color all pixels above a white threshold (in one frame)

3. Make transparent all pixels below threshold (in same frame)

4. Overlay colored frame over uncolored frame

21


Results







21


Results







21


Results



Some reasonably successful...

at”@p@

22


Results



Some not so much

aú@p@: too much colored

23


Results



Some not so much

at@k@: too little colored

23


Results


Automation: second attempt


2. Split frame to be colored into blocks

3. For each block, if block contains “interesting information”

3.1 Color all pixels above a white threshold3.2 Make transparent all pixels below threshold


24


Results








24


Results








24


Results








24


Results



Hmmm...

at”@p@

25

References

Li, M., Akgul, Y., and Kambhamettu, C. (2005). Edge Trak [Compuer Program]. Version 1.0.0.4. RetrievedOctober 3, 2008, from vims.cis.udel.edu/EdgeTrak/.

Documents

Kaytetye coronal contrasts without …linguistics.berkeley.edu/.../files/Ultrafest_Slides_13.pdfKaytetye coronal contrasts without edge-detection Susan Lin, Benjamin Davies, and Katherine