TY - DATA T1 - Differential diagnosis of apraxia of speech (Basilakos et al., 2017) PY - 2017/11/27 AU - Alexandra Basilakos AU - Grigori Yourganov AU - Dirk-Bart den Ouden AU - Daniel Fogerty AU - Chris Rorden AU - Lynda Feenaughty AU - Julius Fridriksson UR - https://asha.figshare.com/articles/dataset/Differential_diagnosis_of_apraxia_of_speech_Basilakos_et_al_2017_/5611309 DO - 10.23641/asha.5611309.v1 L4 - https://ndownloader.figshare.com/files/9792544 L4 - https://ndownloader.figshare.com/files/9792529 L4 - https://ndownloader.figshare.com/files/9792532 L4 - https://ndownloader.figshare.com/files/9792535 L4 - https://ndownloader.figshare.com/files/9792538 L4 - https://ndownloader.figshare.com/files/9792541 KW - apraxia KW - apraxia of speech KW - diagnosis KW - adults KW - stroke KW - aphasia KW - acoustic measures KW - differential diagnosis KW - motor speech disorder KW - Linguistic Processes (incl. Speech Production and Comprehension) N2 - 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 Material S1. Demarcation of vocalic and consonantal boundaries for calculating the normalized Pairwise Variability Index–Vowels (nPVI-V).Supplemental Material S2. Narrow transcription codes used to quantify distortion errors.Supplemental Material S3. Criteria for preprocessing speech samples for envelope modulation spectrum (EMS) analyses using Adobe Soundbooth.Supplemental Material S4. Correlation coefficients for each predictor variable by group.Supplemental Material S5. Table S1. Correlations between predictor variables and ASRS scores for the AOS-Aphasia group only.Supplemental Material S6. LDA results with aphasia severity included.Basilakos, A., Yourganov, G., den Ouden, D.-B., Fogerty, D., Rorden, C., Feenaughty, L., & Fridriksson, J. (2017). A multivariate analytic approach to the differential diagnosis of apraxia of speech. Journal of Speech, Language, and Hearing Research, 60, 3378–3392. https://doi.org/10.1044/2017_JSLHR-S-16-0443 ER -