The clinical assessment of speech abnormalities in Cerebellar Ataxia (CA) is time-consuming and inconsistent. We have developed an automated objective system to quantify CA severity and thereby facilitate remote monitoring and optimisation of therapeutic interventions. A quantitative acoustic assessment could prove to be a viable biomarker for this purpose. Our study explores the use of phase-based cepstral features extracted from the modified group delay function as a complement to the features obtained from the magnitude cepstrum. We selected a combination of 15 acoustic measurements using RELIEF feature selection algorithm during the feature optimisation process. These features were used to segregate ataxic speakers from normal speakers (controls) and objectively assess them based on their severity. The effectiveness of our study has been experimentally evaluated through a clinical study involving 42 patients diagnosed with CA and 23 age-matched controls. A radial basis function kernel based support vector machine (SVM) classifier achieved a classification accuracy of 84.6% in CA-Control discrimination [area under the ROC curve (AUC) of 0.97] and 74% in the modified 3-level CA severity estimation (AUC of 0.90) deduced from the clinical ratings. The strong classification ability of selected features and the SVM model supports this scheme's suitability for monitoring CA related speech motor abnormalities.

中文翻译：

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小脑共济失调语音的定量评估基于幅度和相位的倒谱。

小脑性共济失调（CA）语音异常的临床评估既耗时又不一致。我们已经开发了一个自动化的客观系统来量化CA严重程度，从而促进远程监控和优化治疗干预。为此，定量声学评估可能是可行的生物标记。我们的研究探索了使用从修改后的群延迟函数中提取的基于相位的倒谱特征作为对从倒谱幅度获得的特征的补充。在特征优化过程中，我们使用RELIEF特征选择算法选择了15种声学测量的组合。这些功能用于将共济失调者与正常人（对照组）区分开，并根据其严重程度客观地对其进行评估。通过一项涉及42位被诊断为CA的患者和23位年龄匹配的对照的临床研究，通过实验评估了我们研究的有效性。基于径向基函数核的支持向量机（SVM）分类器在CA-Control判别[ROC曲线下的面积（AUC）为0.97]时达到84.6％的分类准确度，在修正的3级CA严重性估计中达到74％的分类准确度（根据临床评分推算出AUC为0.90）。所选功能的强大分类能力和SVM模型支持该方案适用于监视CA相关的语音运动异常。从临床评分中推论出，CA-Control的歧视率[ROC曲线下面积（AUC）为0.97]为6％，修改后的3级CA严重度估计值（AUC为0.90）为74％。所选功能的强大分类能力和SVM模型支持该方案适用于监视CA相关的语音运动异常。从临床评分中推论出，CA-Control的歧视率[ROC曲线下面积（AUC）为0.97]为6％，修改后的3级CA严重度估计值（AUC为0.90）为74％。所选功能的强大分类能力和SVM模型支持该方案适用于监视CA相关的语音运动异常。