To assist the clinical diagnosis and treatment of speech dysarthria, automatic dysarthric speech detection techniques providing reliable and cost-effective assessment are indispensable. Based on clinical evidence on spectro-temporal distortions associated with dysarthric speech, we propose to automatically discriminate between healthy and dysarthric speakers exploiting spectro-temporal subspaces of speech. Spectro-temporal subspaces are extracted using singular value decomposition, and dysarthric speech detection is achieved by applying a subspace-based discriminant analysis. Experimental results on databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed subspace-based approach using temporal subspaces is more advantageous than using spectral subspaces, also outperforming several state-of-the-art automatic dysarthric speech detection techniques.

中文翻译：

---

用于自动构音障碍语音检测的基于子空间的学习

为了协助语音构音障碍的临床诊断和治疗，提供可靠且具有成本效益的评估的自动构音障碍语音检测技术是必不可少的。基于与构音障碍语音相关的谱时间失真的临床证据，我们建议利用语音的谱时间子空间自动区分健康和构音障碍说话者。使用奇异值分解提取谱时间子空间，并通过应用基于子空间的判别分析实现构音障碍语音检测。针对不同语言和病理的健康和构音障碍者数据库的实验结果表明，所提出的使用时间子空间的基于子空间的方法比使用频谱子空间更有利，