Romana Amrit, Bandon John, Carlozzi Noelle, Roberts Angela, Provost Emily Mower
Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Physical Medicine & Rehabilitation, University of Michigan, Ann Arbor, Michigan, USA.
Interspeech. 2020 Oct;2020:4966-4970. doi: 10.21437/interspeech.2020-2724.
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.
亨廷顿舞蹈症(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(如步态、言语、声音)。由于其渐进性,HD治疗需要持续对症状进行临床监测。携带导致HD的亨廷顿基因突变的个体,在从症状前阶段发展到症状显现阶段的过程中,可能会出现一系列言语症状。基于言语的被动监测有可能通过更持续地跟踪症状表现来增加临床信息。区分症状前和症状显现阶段的HD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变化来区分症状前和症状显现阶段的HD。为此,我们聚焦于HD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失真测量指标(FVDM)。我们表明,FVDM与现有文献中的特征相结合,能够以80%的准确率区分症状前和症状显现阶段的HD。