Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples.
Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples.
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DOI：
10.21437/slpat.2016-16
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Green JR
中科院分区：
文献类型：
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作者：
Wang J;Kothalkar PV;Kim M;Yunusova Y;Campbell TF;Heitzman D;Green JR
肌萎缩侧索硬化症（amyotrophiclateralsclerosis，ALS）是一种进展迅速的神经系统疾病，它影响语言运动功能，导致构音障碍（dysarthria），一种运动性语言障碍。言语和清晰度恶化是ALS疾病进展的指标;及时监测疾病进展对于这些患者的临床管理至关重要。本文研究了机器预测的可理解的说话率的9个人与ALS的基础上，少量的语音声学和发音样本。两种特征选择技术-决策树和梯度提升-与支持向量回归用于预测可懂说话率。实验结果表明，仅从少量的语音样本预测可懂语速的可行性。此外，当决策树用作特征选择技术时，将发音特征添加到声学特征中提高了预测性能。
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurological disease that affects the speech motor functions, resulting in dysarthria, a motor speech disorder. Speech and articulation deterioration is an indicator of the disease progression of ALS; timely monitoring of the disease progression is critical for clinical management of these patients. This paper investigated machine prediction of intelligible speaking rate of nine individuals with ALS based on a small number of speech acoustic and articulatory samples. Two feature selection techniques - decision tree and gradient boosting - were used with support vector regression for predicting the intelligible speaking rate. Experimental results demonstrated the feasibility of predicting intelligible speaking rate from only a small number of speech samples. Furthermore, adding articulatory features to acoustic features improved prediction performance, when decision tree was used as the feature selection technique.