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. 2017 Dec 20;60(12):3378-3392.
doi: 10.1044/2017_JSLHR-S-16-0443.

A Multivariate Analytic Approach to the Differential Diagnosis of Apraxia of Speech

Alexandra Basilakos  1 , Grigori Yourganov  2 , Dirk-Bart den Ouden  1 , Daniel Fogerty  1 , Chris Rorden  2   3 , Lynda Feenaughty  1   4 , Julius Fridriksson  1   3
Affiliations

A Multivariate Analytic Approach to the Differential Diagnosis of Apraxia of Speech

Alexandra Basilakos et al. J Speech Lang Hear Res. .

Abstract

Purpose: Apraxia of speech (AOS) is a consequence of stroke that frequently co-occurs with aphasia. Its study is limited by difficulties with its perceptual evaluation and dissociation from co-occurring impairments. This study examined the classification accuracy of several acoustic measures for the differential diagnosis of AOS in a sample of stroke survivors.

Method: Fifty-seven individuals were included (mean age = 60.8 ± 10.4 years; 21 women, 36 men; mean months poststroke = 54.7 ± 46). Participants were grouped on the basis of speech/language testing as follows: AOS-Aphasia (n = 20), Aphasia Only (n = 24), and Stroke Control (n = 13). Normalized Pairwise Variability Index, proportion of distortion errors, voice onset time variability, and amplitude envelope modulation spectrum variables were obtained from connected speech samples. Measures were analyzed for group differences and entered into a linear discriminant analysis to predict diagnostic classification.

Results: Out-of-sample classification accuracy of all measures was over 90%. The envelope modulation spectrum variables had the greatest impact on classification when all measures were analyzed together.

Conclusions: This study contributes to efforts to identify objective acoustic measures that can facilitate the differential diagnosis of AOS. Results suggest that further study of these measures is warranted to determine the best predictors of AOS diagnosis.

Supplemental materials: https://doi.org/10.23641/asha.5611309.

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Figures

Figure 1.
Figure 1.
Example EMS plot for a typical speaker. The top waveform depicts the raw audio file, the middle portion depicts the extracted amplitude envelope, and the bottom portion displays the modulation index for each modulation band. EMS = envelope modulation spectrum.
Figure 2.
Figure 2.
Box plots of the proportion of nPVI-V (Panel A), distortion errors (Panel B), and VOT variability for voiced (Panel C) and voiceless (Panel D) initial stop consonants. Horizontal lines indicate group differences at Bonferroni-corrected p level of .0167. Outliers are indicated by the x. SC = Stroke Control; AO = Aphasia Only; AOS-A = Apraxia of Speech with Concomitant Aphasia; nPVI-V = normalized Pairwise Variability Index–Vowels; VOT-SD = standard deviation of voice onset time.
Figure 3.
Figure 3.
Amplitude energy for each of the EMS bands tested. Horizontal lines indicate significant group differences at the Bonferroni-corrected p level of .0167. EMS = envelope modulation spectrum; SC = Stroke Control; AO = Aphasia Only; AOS-A = Apraxia of Speech with Concomitant Aphasia.
Figure 4.
Figure 4.
Results from the linear discriminant analysis (LDA) with all variables and all participants included. Variables with negative weights indicate that a higher score on that variable is predictive of AOS diagnosis (according to the Apraxia of Speech Rating Scale [ASRS]). Variables with positive weights indicate that a higher score on that variable was not associated with AOS diagnosis. AOS = apraxia of speech; nPVI-V = normalized Pairwise Variability Index–Vowels; VOT-SD = standard deviation of voice onset time.
See this image and copyright information in PMC

References

    1. Arvaniti A. (2012). The usefulness of metrics in the quantification of speech rhythm. Journal of Phonetics, 40(3), 351–373.
    1. Ballard K. J., Azizi L., Duffy J., McNeil M. R., Halaki M., O'Dwyer N., … Robin D. A. (2016). A predictive model for diagnosing stroke-related apraxia of speech. Neuropsychologia, 81, 129–139. - PubMed
    1. Ballard K. J., Granier J. P., & Robin D. A. (2000). Understanding the nature of apraxia of speech: Theory, analysis and treatment. Aphasiology, 14(10), 969–995.
    1. Basilakos A., Rorden C., Bonilha L., Moser D., & Fridriksson J. (2015). Patterns of poststroke brain damage that predict speech production errors in apraxia of speech and aphasia dissociate. Stroke, 46(6), 1561–1566. https://doi.org/10.1161/STROKEAHA.115.009211 - PMC - PubMed
    1. Boersma P., & Weenink D. (2001). Praat: A system for doing phonetics by computer [Computer program]. Retrieved from http://www.praat.org

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