Predicting Intelligibility Gains in Dysarthria Through Automated Speech Feature Analysis
Predicting Intelligibility Gains in Dysarthria Through Automated Speech Feature Analysis
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DOI：
10.1044/2017_jslhr-s-16-0453
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发表时间：
2017-11-01
影响因子：
2.6
通讯作者：
Liss, Julie M.
中科院分区：
文献类型：
--
作者：
Fletcher, Annalise R.;Wisler, Alan A.;Liss, Julie M.
目的：构音障碍患者的言语行为改变对言语清晰度有不同的影响。在配套文章中，发现说话者的基线语音测量与他们在说话更大声和降低速率的提示下的可懂度增益之间存在显着关系（弗莱彻，麦考利夫，Lansford，Sinex，&利斯，2017）。本研究重新审视这些功能，并评估是否自动声学评估也可以用来预测可懂度gains.Method：50扬声器（7个老年人和43与构音障碍）读一段习惯，大声，和缓慢的说话模式。自动测量的长期平均频谱，包络调制频谱，梅尔频率倒谱系数提取短段参与者的基线语音。可懂度增益进行统计建模，并使用交叉验证评估基线语音测量的预测能力。结果：统计模型可以预测未接受训练的扬声器的可懂度增益。自动声学特征能够更好地预测扬声器在大声条件下的改善，而不是配套文章中报告的手动测量。结论：这些声学分析为快速评估治疗方案提供了一种有前途的工具。基线语音模式的自动化测量可以实现更具选择性的纳入标准和治疗研究中更强的组结果。
Purpose: Behavioral speech modifications have variable effects on the intelligibility of speakers with dysarthria. In the companion article, a significant relationship was found between measures of speakers' baseline speech and their intelligibility gains following cues to speak louder and reduce rate (Fletcher, McAuliffe, Lansford, Sinex, & Liss, 2017). This study reexamines these features and assesses whether automated acoustic assessments can also be used to predict intelligibility gains.Method: Fifty speakers (7 older individuals and 43 with dysarthria) read a passage in habitual, loud, and slow speaking modes. Automated measurements of long- term average spectra, envelope modulation spectra, and Melfrequency cepstral coefficients were extracted from short segments of participants' baseline speech. Intelligibility gains were statistically modeled, and the predictive power of the baseline speech measures was assessed using cross-validation.Results: Statistical models could predict the intelligibility gains of speakers they had not been trained on. The automated acoustic features were better able to predict speakers' improvement in the loud condition than the manual measures reported in the companion article.Conclusions: These acoustic analyses present a promising tool for rapidly assessing treatment options. Automated measures of baseline speech patterns may enable more selective inclusion criteria and stronger group outcomes within treatment studies.