Classification of Manifest Huntington Disease using Vowel Distortion Measures.

Huntington disease (HD) is a fatal autosomal dominant neurocognitive disorder that causes cognitive disturbances, neuropsychiatric symptoms, and impaired motor abilities (e.g., gait, speech, voice). Due to its progressive nature, HD treatment requires ongoing clinical monitoring of symptoms. Individuals with the Huntingtin gene mutation, which causes HD, may exhibit a range of speech symptoms as they progress from premanifest to manifest HD. Speech-based passive monitoring has the potential to augment clinical information by more continuously tracking manifestation symptoms. Differentiating between premanifest and manifest HD is an important yet under-studied problem, as this distinction marks the need for increased treatment. In this work we present the first demonstration of how changes in speech can be measured to differentiate between premanifest and manifest HD. To do so, we focus on one speech symptom of HD: distorted vowels. We introduce a set of Filtered Vowel Distortion Measures (FVDM) which we extract from read speech. We show that FVDM, coupled with features from existing literature, can differentiate between premanifest and manifest HD with 80% accuracy.

亨廷顿病（HD）是一种致命的常染色体显性神经认知障碍，会导致认知障碍、神经精神症状以及运动能力受损（例如步态、言语、发声）。由于其进行性的特点，亨廷顿病的治疗需要对症状进行持续的临床监测。携带导致亨廷顿病的亨廷顿基因突变的个体，在从临床前期发展到临床期亨廷顿病的过程中，可能会表现出一系列的言语症状。基于言语的被动监测有可能通过更持续地追踪症状表现来增加临床信息。区分临床前期和临床期亨廷顿病是一个重要但研究不足的问题，因为这种区分标志着需要加强治疗。在这项工作中，我们首次展示了如何通过测量言语的变化来区分临床前期和临床期亨廷顿病。为此，我们聚焦于亨廷顿病的一种言语症状：元音扭曲。我们引入了一组从朗读语音中提取的滤波元音扭曲度量（FVDM）。我们表明，FVDM与现有文献中的特征相结合，能够以80%的准确率区分临床前期和临床期亨廷顿病。