Kaytetye coronal contrasts without contours

Transcription

1 Kaytetye coronal contrasts without contours Susan Lin, Benjamin Davies, and Katherine Demuth ARC Centre for Excellence in Cognition and its Disorders Macquarie University October 6, 2013 Ultrafest VI Edinburgh

2 Kaytetye coronal contrasts without edge-detection Susan Lin, Benjamin Davies, and Katherine Demuth ARC Centre for Excellence in Cognition and its Disorders Macquarie University October 6, 2013 Ultrafest VI Edinburgh

3 Why use ultrasound? Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Benefits Good spacial resolution Reasonable temporal resolution (and improving!) Portability Immediacy of visual information 2

4 Why use ultrasound? Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Benefits Good spacial resolution Reasonable temporal resolution (and improving!) Portability Immediacy of visual information Consequence Portable ultrasound machines are being employed in fieldwork and documentation, in increasing numbers. 2

5 Typical procedures Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case 1. Collect 3

6 Typical procedures Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case 1. Collect 2. Post-process 3

7 Typical procedures Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case 1. Collect 2. Post-process Extract relevant frames Edge-detection of contours 3. Analyze 3

8 Typical procedures Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case 1. Collect 2. Post-process Extract relevant frames Edge-detection of contours 3. Analyze 4. Interpret 3

9 Typical procedures Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case 1. Collect 2. Post-process Extract relevant frames Edge-detection of contours 3. Analyze 4. Interpret 3

10 Typical procedures Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case 1. Collect 2. Post-process Extract relevant frames Edge-detection of contours 3. Analyze 4. Interpret Post-processing may be time-consuming Infrastructure cost (in time) high for small projects and/or projects with simple questions Processing time may be too high for performing while on site 3

11 Typical procedures Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case 1. Collect 2. Post-process Extract relevant frames Edge-detection of contours 3. Analyze 4. Interpret Post-processing may be time-consuming Infrastructure cost (in time) high for small projects and/or projects with simple questions Processing time may be too high for performing while on site 3

12 Reducing the barrier to entry Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Question How can we reduce the barrier to entry (whether real or perceived)? 4

13 Reducing the barrier to entry Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Question How can we reduce the barrier to entry (whether real or perceived)? At least two ways: 4

14 Reducing the barrier to entry Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Question How can we reduce the barrier to entry (whether real or perceived)? At least two ways: Speed up or automate edge-detection 4

15 Reducing the barrier to entry Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Question How can we reduce the barrier to entry (whether real or perceived)? At least two ways: Speed up or automate edge-detection Develop alternatives to edge-detection 4

16 Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case An observation (from a colleague)./images/poster.jpg ang@n@na 5

17 Testing ground: Kaytetye Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Arandic language Spoken 300km north of Alice Springs, NT Dozens of speakers remaining 6

18 Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Kaytetye coronal inventory (phonemic) dental alveolar retroflex palatal pre-palatal oral stop t t ú c j c nasal n n ï ñ j ñ pre-stopped nasal t n t n ú ï c ñ jc ñ lateral l l í L j L 7

19 Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Kaytetye coronal inventory (phonemic) dental alveolar retroflex palatal pre-palatal oral stop t t ú c j c nasal n n ï ñ j ñ pre-stopped nasal t n t n ú ï c ñ jc ñ lateral l l í L j L 7

20 Ultrasound in practice A place for qualitative observations? Kaytetye coronal contrast: a test case Kaytetye coronal inventory (phonemic) dental alveolar retroflex palatal pre-palatal oral stop t t ú c j c nasal n n ï ñ j ñ pre-stopped nasal t n t n ú ï c ñ jc ñ lateral l l í L j L Test question Does production of alveolar compared to dental stops in Kaytetye involve different motion of the tongue? 7

21 Introduction Data Sighted Measured Stimuli I Target stops elicited in /#a a/ or contexts I 8-12 repetitions of each item I 7 female native speakers of Kaytetye (all multi-lingual to some extent) I Probe held by researcher 8

22 Frames extracted Introduction Data Sighted Measured 1 vocalic frame, from preceding vowel (a) 9

23 Frames extracted Introduction Data Sighted Measured 1 vocalic frame, from preceding vowel (a) 1 consonantal frame, at maximal constriction of target consonant 9

24 Post-processing: sighted Data Sighted Measured at 10

25 Post-processing: sighted Data Sighted Measured at 1. Color consonantal frame (red) 2. Turn blacks in consonantal frame transparent 10

26 Post-processing: sighted Data Sighted Measured at 1. Color consonantal frame (red) 2. Turn blacks in consonantal frame transparent 3. Combine frames 10

27 Heights sighted Introduction Data Sighted Measured Naive coder instructed to mentally trisect the white line in the center of the image and rate +1 if red line is further from center than white 0 if red line is the same distance as the white from center -1 if red line is closer to center than white 11

28 Heights sighted Introduction Data Sighted Measured +1 FRONT -1 MID 0 BACK Naive coder instructed to mentally trisect the white line in the center of the image and rate +1 if red line is further from center than white 0 if red line is the same distance as the white from center -1 if red line is closer to center than white 11

29 Data Sighted Measured Post-processing: measured at Edge-detect both vocalic and consnoantal contours via EdgeTrak (Li et al., 2005) 12

30 Data Sighted Measured Post-processing: measured at Edge-detect both vocalic and consnoantal contours via EdgeTrak (Li et al., 2005) Define three radial lines roughly approximating tongue tip/blade (FRONT), body (MID), and back (BACK) 12

31 Distances measured Data Sighted Measured -4 FRONT -21 MID 21 BACK Distance along radial lines from vocalic (black) to consonantal (red) contours measured (pixels) Positive polarity if consonantal contour further from center than vocalic 13

32 : measured Introduction Measured vs. sighted results Measured vs. sighted values Test question Does production of alveolar compared to dental stops in Kaytetye involve different motion of the tongue? Distance between V and C (px) Back Distance between V and C (px) Middle Distance between V and C (px) Front alveolar dental alveolar dental alveolar dental Place Place Place 14

33 : measured Introduction Measured vs. sighted results Measured vs. sighted values Test question Does production of alveolar compared to dental stops in Kaytetye involve different motion of the tongue? Yes. Distance between V and C (px) Back Distance between V and C (px) Middle Distance between V and C (px) Front alveolar dental alveolar dental alveolar dental Place Place Place n.s. * p < 0.05 * p <

34 : sighted Introduction Measured vs. sighted results Measured vs. sighted values Test question Does production of alveolar compared to dental stops in Kaytetye involve different motion of the tongue? Back Middle Front Height Responses Height Responses Height Responses alveolar dental alveolar dental alveolar dental Place Place Place 15

35 : sighted Introduction Measured vs. sighted results Measured vs. sighted values Test question Does production of alveolar compared to dental stops in Kaytetye involve different motion of the tongue? Yes, but only in MID region. Back Middle Front Height Responses Height Responses Height Responses alveolar dental alveolar dental alveolar dental Place Place Place n.s. * p < 0.05 n.s. 15

36 vs. results Measured vs. sighted results Measured vs. sighted values Measured Means Sighted Means BACK MID* FRONT* BACK MID* FRONT Alveolar Dental

37 vs. results Measured vs. sighted results Measured vs. sighted values Measured Means Sighted Means BACK MID* FRONT* BACK MID* FRONT Alveolar Dental comparison Sighted ratings capable of distinguishing between polarity 16

38 vs. results Measured vs. sighted results Measured vs. sighted values Measured Means Sighted Means BACK MID* FRONT* BACK MID* FRONT Alveolar Dental comparison Sighted ratings capable of distinguishing between polarity, but are not great at expressing variation in magnitude. 16

39 Values vs. values Introduction Measured vs. sighted results Measured vs. sighted values Back Mid Front Distance (px) Distance (px) Distance (px) Height Responses Height Responses 0 1 Height Responses 1 < 0 < +1 1 < 0 < +1 n.s. 17

40 Values vs. values Introduction Measured vs. sighted results Measured vs. sighted values Back Mid Front Distance (px) Distance (px) Distance (px) Height Responses Height Responses 0 1 Height Responses 1 < 0 < +1 1 < 0 < +1 n.s. Values direct comparison Sighted rating is a reasonably good predictor for measured distances. 17

41 Summary Introduction Usability Viability Automation Suitable for First glances Small projects Visualization Teaching / demonstration? 18

42 Summary Introduction Usability Viability Automation Suitable for First glances Small projects Visualization Teaching / demonstration? Not suitable for Measuring distances automatically Comparisons involving small effects Projects with large amounts of data 18

43 Usability Viability Automation Typical procedures (revisited) Time investment Where can we save time? 1. Collect 2. Post-process Extract relevant frames Edge-detection 3. Analyze 4. Interpret 19

44 Usability Viability Automation Typical procedures (revisited) Time investment Where can we save time? 1. Collect 2. Post-process Extract relevant frames Edge-detection 3. Analyze 4. Interpret 19

45 Usability Viability Automation Qualitative vs. quantitative methods 20

46 Usability Viability Automation Qualitative vs. quantitative methods Analysis: quantitative Sighted (qualitative) human component Measured (quantitative) can be largely automated 20

47 Usability Viability Automation Qualitative vs. quantitative methods Edge-detection or alternative:? Measured (quantitative) time-consuming Sighted (qualitative) can be automated? Analysis: quantitative Sighted (qualitative) human component Measured (quantitative) can be largely automated 20

48 Usability Viability Automation Qualitative vs. quantitative methods Edge-detection or alternative:? Measured (quantitative) time-consuming Sighted (qualitative) can be automated? Analysis: quantitative Sighted (qualitative) human component Measured (quantitative) can be largely automated Automation If qualitative methods are to be competitive, automation must be made possible. 20

49 Usability Viability Automation Automation: first (naive) attempt 1. Increase contrast of both frames 21

50 Usability Viability Automation 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) 21

51 Usability Viability Automation 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

52 Usability Viability Automation Automation: first (naive) attempt Some reasonably successful... at 22

53 Usability Viability Automation Automation: first (naive) attempt Some not so much too much colored 23

54 Usability Viability Automation Automation: first (naive) attempt Some not so much too little colored 23

55 Usability Viability Automation Automation: second attempt 1. Increase contrast of both frames 24

56 Usability Viability Automation Automation: second attempt 1. Increase contrast of both frames 2. Split frame to be colored into blocks 3. For each block, if block contains interesting information 24

57 Usability Viability Automation Automation: second attempt 1. Increase contrast of both frames 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 threshold 3.2 Make transparent all pixels below threshold 24

58 Usability Viability Automation Automation: second attempt 1. Increase contrast of both frames 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 threshold 3.2 Make transparent all pixels below threshold 4. Overlay colored frame over uncolored frame 24

59 Usability Viability Automation Automation: second attempt Hmmm... at 25

60 References Li, M., Akgul, Y., and Kambhamettu, C. (2005). Edge Trak [Compuer Program]. Version Retrieved October 3, 2008, from vims.cis.udel.edu/edgetrak/.