Classification of Huntington Disease Using Acoustic and Lexical Features

Matthew Perez, Wenyu Jin, Duc Le, Noelle Carlozzi, Praveen Dayalu, Angela Roberts, Emily Mower Provost


Speech is a critical biomarker for Huntington Disease (HD), with changes in speech increasing in severity as the disease progresses. Speech analyses are currently conducted using either transcriptions created manually by trained professionals or using global rating scales. Manual transcription is both expensive and time-consuming and global rating scales may lack sufficient sensitivity and fidelity. Ultimately, what is needed is an unobtrusive measure that can cheaply and continuously track disease progression. We present first steps towards the development of such a system, demonstrating the ability to automatically differentiate between healthy controls and individuals with HD using speech cues. The results provide evidence that objective analyses can be used to support clinical diagnoses, moving towards the tracking of symptomatology outside of laboratory and clinical environments.


 DOI: 10.21437/Interspeech.2018-2029

Cite as: Perez, M., Jin, W., Le, D., Carlozzi, N., Dayalu, P., Roberts, A., Mower Provost, E. (2018) Classification of Huntington Disease Using Acoustic and Lexical Features. Proc. Interspeech 2018, 1898-1902, DOI: 10.21437/Interspeech.2018-2029.


@inproceedings{Perez2018,
  author={Matthew Perez and Wenyu Jin and Duc Le and Noelle Carlozzi and Praveen Dayalu and Angela Roberts and Emily {Mower Provost}},
  title={Classification of Huntington Disease Using Acoustic and Lexical Features},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={1898--1902},
  doi={10.21437/Interspeech.2018-2029},
  url={http://dx.doi.org/10.21437/Interspeech.2018-2029}
}