Automatic detection of Parkinson's disease using noise measures of speech
Author(s): Belalcazar-Bolaños E, Orozco-Arroyave J, Arias-Londono J, Vargas-Bonilla J, Nöth E
Publication year: 2013
Event: 2013 18th Symposium of Image, Signal Processing, and Artificial Vision, STSIVA 2013
Event location: Bogota
Parkinson's disease (PD) is a neurodegenerative disorder that is characterized by the loss of dopaminergic neurons in the mid brain. It is demonstrated that about 90% of the people with PD also develop speech impairments, exhibiting symptoms such as monotonic speech, low pitch intensity, inappropriate pauses, imprecision in consonants and problems in prosody; although they are already identify problems, only 3% to 4% of the patients receive speech therapy. The research community has addressed the problem of the automatic detection of PD by means of noise measures; however, in such works only the phonation of the English vowel /a/ has been considered. In this paper, the five Spanish vowels uttered by 50 people with PD and 50 healthy controls (HC) are evaluated automatically considering a set of four noise measures: Harmonics to Noise Ratio (HNR), Normalized Noise Energy (NNE), Cepstral HNR (CHNR) and Glottal to Noise Excitation Ratio (GNE). The decision on whether a speech recording is from a person with PD or from a HC is taken by a K nearest neighbors (k-NN) classifier, finding an accuracy of 66.57% when only the vowel /i/ is considered. © 2013 IEEE.
FAU Authors / FAU Editors How to cite
APA: Belalcazar-Bolaños, E., Orozco-Arroyave, J., Arias-Londono, J., Vargas-Bonilla, J., & Nöth, E. (2013). Automatic detection of Parkinson's disease using noise measures of speech.
MLA: Belalcazar-Bolaños, E. A., et al. "Automatic detection of Parkinson's disease using noise measures of speech." Proceedings of the 2013 18th Symposium of Image, Signal Processing, and Artificial Vision, STSIVA 2013, Bogota 2013.