Automatic detection of early stages of Parkinson's disease through acoustic voice analysis with mel-frequency cepstral coefficients

Abstract

Vocal impairments are one of the earliest disrupted modalities in Parkinson's disease (PD). Most of the studies whose aim was to detect Parkinson's disease through acoustic analysis use global parameters. In the meantime, in speaker and speech recognition, analyses are carried out by short-term parameters, and more precisely by Mel-Frequency Cepstral… (More)
DOI: 10.1109/ATSIP.2017.8075567

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Cite this paper

@article{Jeancolas2017AutomaticDO, title={Automatic detection of early stages of Parkinson's disease through acoustic voice analysis with mel-frequency cepstral coefficients}, author={Laetitia Jeancolas and Habib Benali and Badr-Eddine Benkelfat and Graziella Mangone and Jean-Christophe Corvol and Marie Vidailhet and St{\'e}phane Leh{\'e}ricy and Dijana Petrovska-Delacr{\'e}taz}, journal={2017 International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)}, year={2017}, pages={1-6} }