TY - JOUR
T1 - Classification of manifest huntington disease using vowel distortion measures
AU - Romana, Amrit
AU - Bandon, John
AU - Carlozzi, Noelle
AU - Roberts, Angela
AU - Provost, Emily Mower
N1 - Funding Information:
We thank the Investigators and Coordinators of this study, the study participants, the Huntington Study Group, and the Huntington’s Disease Society of America. This work was supported by the National Institutes of Health (NIH), National Center for Advancing Translational Sciences (UL1TR000433), the Heinz C Prechter Bipolar Research Fund, and the Richard Tam Foundation at the University of Michigan.
PY - 2020
Y1 - 2020
N2 - 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 understudied 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.
AB - 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 understudied 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.
KW - Disordered speech
KW - Huntington disease
KW - Speech feature extraction
KW - Vowel distortion
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U2 - 10.21437/Interspeech.2020-2724
DO - 10.21437/Interspeech.2020-2724
M3 - Conference article
AN - SCOPUS:85098231064
VL - 2020-October
SP - 4966
EP - 4970
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
SN - 2308-457X
T2 - 21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020
Y2 - 25 October 2020 through 29 October 2020
ER -