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Diagnostic Assessment of Childhood Apraxia of Speech Using Automatic Speech Recognition (ASR) Methods

机译：使用自动语音识别（ASR）方法对儿童言语失用症的诊断评估

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摘要

We report findings from two feasibility studies using automatic speech recognition (ASR) methods in childhood speech sound disorders. The studies evaluated and implemented the automation of two recently proposed diagnostic markers for suspected Apraxia of Speech (AOS) termed the Lexical Stress Ratio (LSR) and the Coefficient of Variation Ratio (CVR). The LSR is a weighted composite of amplitude area, frequency area , and duration in the stressed compared to the unstressed vowel as obtained from a speaker’s productions of eight trochaic word forms. Composite weightings for the three stress parameters were determined from a principal components analysis. The CVR expresses the average normalized variability of durations of pause and speech events that were obtained from a conversational speech sample. We describe the automation procedures used to obtain LSR and CVR scores for four children with suspected AOS and report comparative findings. The LSR values obtained with ASR were within 1.2% to 6.7% of the LSR values obtained manually using Computerized Speech Lab (CSL). The CVR values obtained with ASR were within 0.7% to 2.7% of the CVR values obtained manually using Matlab. These results indicate the potential of ASR-based techniques to process these and other diagnostic markers of childhood speech sound disorders.
机译：我们报告了在儿童语音障碍中使用自动语音识别（ASR）方法的两项可行性研究的结果。这项研究评估并实施了两个新近提出的针对言语失用症（AOS）的诊断标记物的自动化，这些标记物称为词法重音比（LSR）和变异系数（CVR）。 LSR是加权的振幅区域，频率区域和持续时间的加权合成，与从说话人的八种Trochaic字形产生的非应激元音相比。通过主成分分析确定了三个应力参数的复合权重。 CVR表示从会话语音样本获得的暂停和语音事件的持续时间的平均归一化变异性。我们描述了用于获得四个疑似AOS的儿童的LSR和CVR分数的自动化程序，并报告了比较结果。使用ASR获得的LSR值在使用计算机语音实验室（CSL）手动获得的LSR值的1.2％到6.7％之间。用ASR获得的CVR值在使用Matlab手动获得的CVR值的0.7％到2.7％之内。这些结果表明基于ASR的技术有可能处理儿童语音障碍的这些和其他诊断标记。

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