Differential diagnosis of apraxia of speech (Basilakos et al., 2017)

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

目的：言语失用症（AOS）是中风的一种常见并发症，常与失语症伴随出现。由于对其感知评价的困难以及与伴随障碍的分离，其研究受到限制。本研究旨在探讨在中风幸存者样本中，针对AOS进行鉴别诊断的多种声学指标的分类准确性。方法：纳入了57名个体（平均年龄为60.8 ± 10.4岁；女性21人，男性36人；平均中风后月份为54.7 ± 46）。根据言语/语言测试将参与者分为以下几组：AOS-失语症组（n = 20）、仅失语症组（n = 24）和中风对照组（n = 13）。从连续的言语样本中获取了归一化成对差异性指数、失真错误比例、声带起始时间变异性以及幅度包络调制频谱变量。对测量值进行了组间差异分析，并将结果输入到线性判别分析中，以预测诊断分类。结果：所有指标的样本外分类准确率均超过90%。当分析所有指标时，幅度调制频谱变量对分类的影响最大。结论：本研究有助于识别有助于AOS鉴别诊断的客观声学指标。结果提示，有必要对这些指标进行进一步研究，以确定AOS诊断的最佳预测因子。补充材料S1：计算归一化成对差异性指数-元音（nPVI-V）的元音和辅音边界划分。补充材料S2：用于量化失真错误的狭窄转录代码。补充材料S3：使用Adobe Soundbooth对语音样本进行预处理以进行幅度包络调制频谱（EMS）分析的标准。补充材料S4：按组计算的每个预测变量的相关系数。补充材料S5：仅针对AOS-失语症组的预测变量与ASRS评分之间的相关性。补充材料S6：包含失语症严重程度的LDA结果。Basilakos, A., Yourganov, G., den Ouden, D.-B., Fogerty, D., Rorden, C., Feenaughty, L., & Fridriksson, J. (2017). 对言语失用症鉴别诊断的多变量分析方法。言语、语言和听觉研究杂志，60，3378–3392. https://doi.org/10.1044/2017_JSLHR-S-16-0443