title: Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings.
abstract: There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.
authors: Betul Erdogdu
authors: BE
authors: Sakar
authors: M Erdem
authors: ME
authors: Isenkul
authors: C Okan
authors: CO
authors: Sakar
authors: Ahmet
authors: A
authors: Sertbas
authors: Fikret
authors: F
authors: Gurgen
authors: Sakir
authors: S
authors: Delil
authors: Hulya
authors: H
authors: Apaydin
authors: Olcay
authors: O
authors: Kursun
mesh_terms: D000328:Adult; D000368:Aged; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013018:Sound Spectrography / Q000379:methods*; D013060:Speech / Q000502:physiology*; D060388:Support Vector Machine; D014831:Voice / Q000502:physiology*