An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
Abstract
:1. Introduction
1.1. Lexical Stress
1.2. Measuring Lexical Stress
1.3. Automated Analysis of Lexical Stress
- Peak-to-peak Teager energy operator (TEO) amplitude over syllable nucleus;
- mean TEO energy over syllable nucleus;
- maximum TEO energy over syllable nucleus;
- nucleus duration;
- syllable duration;
- maximum f0 over syllable nucleus;
- mean f0 over syllable nucleus;
- 27 Mel-scale energy bands over syllable nucleus.
1.4. Purpose
- An automated lexical stress classifier using acoustic features of duration, f0, intensity, and spectral energy across adjacent syllables in polysyllabic words will achieve:
- (a)
- ≥80% agreement with human perceptual judgments for TD speech;
- (b)
- Higher classification accuracy for TD speakers than for CAS speakers, for whom the likelihood of mispronunciation is high;
- (c)
- Higher classification accuracy when using a knowledge-driven system trained on the segmental errors represented in the disordered speech sample.
- Classification errors will be associated with within-word features known to reduce human inter-rater reliability, such as equivocal stress across the first two syllables (e.g., HAMBURger/ˈhæmˈbɜgʌ/); short-vowel phonemes in the stressed syllable (e.g., BUTterfly/ˈbʌtəˌflaɪ); ambiguous phoneme boundaries (i.e., liquid consonants at syllable onsets or offsets such as in “elephant”); or words in which weak syllables have low intensity and/or undetectable pitch (i.e., unstressed vowels between two unvoiced phonemes, such as “potato”).
2. Materials and Methods
2.1. Participants
2.2. Stimuli
2.3. Procedure
2.3.1. Forced Alignment
2.3.2. Feature Extraction
2.3.3. Concatenate Raw Features into 1 Wide Feature Vector
2.3.4. DNN Classifier
2.4. Statistical Analysis
3. Results
3.1. Agreement between Classifier and Human Judgment
3.2. Linear Mixed Effects Modelling
3.3. Words Perceived with Correct or Incorrect Lexical Stress
3.4. Confidence Estimates and Within-Word Features
3.5. Age and Severity
4. Discussion
Limitations and Future Directions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Variable | TD (n = 16) | CAS (n = 26) | Statistics a | ||
---|---|---|---|---|---|
M (SD) | Range | M (SD) | Range | ||
Demographic | |||||
Age (years) | 6.1 (2.0) | 4–10 | 5.9 (2.5) | 4–12 | Z = −0.71 ns |
Sex | 7 male 9 female | 22 male 4 female | χ = 7.7 * | ||
Test of Polysyllables b | |||||
PPC | 95.2 (4.2) | 85.6–99.3 | 61.8 (21.1) | 23.9–96.7 | t = 6.24 ** |
PCC | 95.4 (4.8) | 81.4–100 | 57.5 (24.7) | 13.0–98.6 | t = 5.66 ** |
PVC | 93.9 (5.3) | 82.5–100 | 67.5 (17.6) | 38.5–94.2 | t = 5.82 ** |
% Lexical stress matches | 88.8 (8.4) | 77.3–100 | 51.0 (26.6) | 6.3–93.8 | t = 5.5 ** |
Severity Rating c | |||||
Typical–mild | 15/16 | 5/26 | |||
Mild–moderate | 1 d | 5 | |||
Moderate–severe | 0 | 5 | |||
Severe | 0 | 11 |
Feature | Description |
---|---|
f1 | Peak-to-peak TEO amplitude over syllable nucleus |
f2 | Mean TEO energy over syllable nucleus |
f3 | Maximum TEO energy over syllable nucleus |
f4 | Nucleus duration |
f5 | Syllable duration |
f6 | Maximum pitch over syllable nucleus |
f7 | Mean pitch over syllable nucleus |
f8 | 27 Mel-scale energy bands over syllable nucleus |
Source | Numerator df | Denominator df | F | p |
---|---|---|---|---|
Intercept | 1 | 49.346 | 365.151 | <0.001 |
Group | 1 | 32.858 | 18.645 | <0.001 |
Stress | 1 | 35.302 | 20.836 | <0.001 |
Model | 1 | 36.087 | 1.235 | 0.274 |
Age (covariate) | 1 | 38.518 | 8.208 | 0.007 |
Group × Stress | 1 | 35.328 | 4.314 | 0.045 |
Group × Model | 1 | 36.084 | 0.118 | 0.733 |
Stress × Model | 1 | 34.934 | 4.007 | 0.053 |
Group × Stress × Model | 1 | 34.946 | 0.715 | 0.404 |
Source | Numerator df | Denominator df | F | p |
---|---|---|---|---|
Intercept | 1 | 29.545 | 88.358 | <0.001 |
Stress | 1 | 20.894 | 22.864 | <0.001 |
Model | 1 | 22.420 | 0.214 | 0.648 |
PPC (covariate) | 1 | 23.196 | 9.529 | 0.005 |
Stress × Model | 1 | 20.592 | 3.362 | 0.081 |
Single Pronunciation | Multiple Pronunciation | |||||
---|---|---|---|---|---|---|
Comparison | Statistic | p | g | Statistic | p | g |
All (excl. SS) | U = 26 z = 4.95 | <0.0001 | 3.215 | U = 25 z = 4.98 | <0.0001 | 3.617 |
SW | U = 0 z = 5.48 | <0.0001 | 7.468 | U = 0 z = 5.48 | <0.0001 | 6.683 |
WS | U = 131.5 z = 0 | 1 | 0.079 | U = 78.5 z = 1.17 | 0.242 | 0.430 |
Classification Accuracy for TD | Classification Accuracy for CAS | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Single Pronunciation | Multiple Pronunciation | Single Pronunciation | Multiple Pronunciation | |||||||||
Confidence a | ||||||||||||
Single pronunciation | 0.73 ** | — | 0.39 ** | — | ||||||||
Multiple pronunciation | — | 0.35 * | — | 0.58 ** | ||||||||
Segmental features a | ||||||||||||
Nasal phoneme adjacent to vowel | −0.05 | −0.13 | −0.14 | −0.12 | ||||||||
Liquid/glide adjacent to vowel | −0.28 * | −0.27 | −0.21 | −0.26 | ||||||||
Nonschwa unstressed vowel | −0.35 * | −0.33 * | −0.22 | −0.25 | ||||||||
Long stressed vowel | 0.01 | 0.02 | −0.21 | 0.01 | ||||||||
Unvoiced plosive + schwa vowel | −0.09 | −0.02 | −0.20 | −0.09 | ||||||||
Single Pronunciation | Multiple Pronunciation | Single Pronunciation | Multiple Pronunciation | |||||||||
SW + WS | SW | WS | SW + WS | SW | WS | SW + WS | SW | WS | SW + WS | SW | WS | |
Age b | 0.18 | 0.21 | 0.34 | 0.47 | 0.33 | 0.47 | 0.41 * | 0.22 | 0.56 ** | 0.43 * | 0.31 | 0.52 ** |
Speech disorder severity b | ||||||||||||
PCC | 0.04 | −0.10 | 0.38 | 0.58 * | 0.47 | 0.42 | 0.33 | 0.38 | 0.28 | 0.45 * | 0.46 * | 0.40 * |
PVC | 0.11 | 0.05 | 0.42 | 0.35 | 0.23 | 0.40 | 0.39 * | 0.41 * | 0.34 | 0.50 * | 0.49 * | 0.45 * |
PPC | 0.08 | 0.02 | 0.38 | 0.40 | 0.24 | 0.38 | 0.36 | 0.40 * | 0.31 | 0.48 * | 0.48 * | 0.43 * |
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McKechnie, J.; Shahin, M.; Ahmed, B.; McCabe, P.; Arciuli, J.; Ballard, K.J. An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech. Brain Sci. 2021, 11, 1408. https://doi.org/10.3390/brainsci11111408
McKechnie J, Shahin M, Ahmed B, McCabe P, Arciuli J, Ballard KJ. An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech. Brain Sciences. 2021; 11(11):1408. https://doi.org/10.3390/brainsci11111408
Chicago/Turabian StyleMcKechnie, Jacqueline, Mostafa Shahin, Beena Ahmed, Patricia McCabe, Joanne Arciuli, and Kirrie J. Ballard. 2021. "An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech" Brain Sciences 11, no. 11: 1408. https://doi.org/10.3390/brainsci11111408