构音障碍、言语失用症和神经型言语之间的自动和知觉辨别
Automatic and perceptual discrimination between dysarthria, apraxia of speech, and neurotypical speech
摘要：
在运动性言语障碍（MSD）的背景下，自动技术通常是两类技术，旨在区分构音障碍和神经型言语或构音障碍和言语失用症（AoS）。此外，尽管提出了这样的技术来支持临床医生的感知评估，但从未比较过自动分类准确度和感知分类准确度。在本文中，我们研究了一个三级自动技术和一组手工制作的功能，用于区分构音障碍，AOS和神经型语音。针对多类分类问题，本文提出了一种层次化的方法，而不是采用通常使用的一对一或一对其余的方法。此外，进行知觉研究，其中要求言语和语言病理学家听构音障碍、AoS和神经典型言语的录音，并决定录音属于哪一类。建议的自动技术进行评估相同的录音和自动和感知分类性能进行了比较。所呈现的结果表明，分层分类方法产生了更高的分类精度比基线一对一和一对休息的方法。此外，所呈现的结果表明，自动方法产生了更高的分类精度比语音和语言病理学家的感知评估，展示了在临床实践中集成自动工具的潜在优势。
英文摘要：
Automatic techniques in the context of motor speech disorders (MSDs) are typically two-class techniques aiming to discriminate between dysarthria and neurotypical speech or between dysarthria and apraxia of speech (AoS). Further, although such techniques are proposed to support the perceptual assessment of clinicians, the automatic and perceptual classification accuracy has never been compared. In this paper, we investigate a three-class automatic technique and a set of handcrafted features for the discrimination of dysarthria, AoS and neurotypical speech. Instead of following the commonly used One-versus-One or One-versus-Rest approaches for multi-class classification, a hierarchical approach is proposed. Further, a perceptual study is conducted where speech and language pathologists are asked to listen to recordings of dysarthria, AoS, and neurotypical speech and decide which class the recordings belong to. The proposed automatic technique is evaluated on the same recordings and the automatic and perceptual classification performance are compared. The presented results show that the hierarchical classification approach yields a higher classification accuracy than baseline One-versus-One and One-versus-Rest approaches. Further, the presented results show that the automatic approach yields a higher classification accuracy than the perceptual assessment of speech and language pathologists, demonstrating the potential advantages of integrating automatic tools in clinical practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
