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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 1/2022

09.09.2021

Using novel method: Real Cepstral Discrete Cosine Transform, for detecting Parkinson from multiple system atrophy, other neurological diseases and healthy cases using voice analysis

verfasst von: Achraf Benba, Imane Laaqira, Abdelilah Jilbab, Ahmed Hammouch

Erschienen in: International Journal of Speech Technology | Ausgabe 1/2022

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Abstract

This paper seeks to detect Parkinson’s disease among healthy cases, several neurological diseases, in particular those which are very similar, that is to say representing a parkinsonian syndrome such as multi system atrophy. Early detection based on the phonatory symptoms will offer a possibility to the treatments proposed by the doctors to act effectively on the patient hence the interest of our project. We used several algorithms such as MFCC, PLP and RASTA PLP in addition to our new technique called Real Cepstral Discrete Cosine Transform with an SVM classifier with its different kernels to guarantee a better detection.
Vorheriger Artikel Automatic short utterance speaker recognition using stationary wavelet coefficients of pitch synchronised LP residual
Nächster Artikel Closed-set speaker identification using VQ and GMM based models

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Literatur
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Metadaten
Titel
Using novel method: Real Cepstral Discrete Cosine Transform, for detecting Parkinson from multiple system atrophy, other neurological diseases and healthy cases using voice analysis
verfasst von
Achraf Benba
Imane Laaqira
Abdelilah Jilbab
Ahmed Hammouch
Publikationsdatum
09.09.2021
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 1/2022
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-021-09896-y

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