title: Automatic detection of Parkinson's disease in running speech spoken in three different languages.
abstract: The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.
authors.affiliation: Faculty of Engineering, Universidad de Antioquia, Calle 67 Número 53-108, Medellín 1226, Colombia.
authors.forename: J R
authors.initials: JR
authors.lastname: Orozco-Arroyave
authors.affiliation: Pattern Recognition Lab, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Martensstraβe 3, Erlangen 91058, Germany.
authors.forename: F
authors.initials: F
authors.lastname: Hönig
authors.affiliation: Faculty of Engineering, Universidad de Antioquia, Calle 67 Número 53-108, Medellín 1226, Colombia.
authors.forename: J D
authors.initials: JD
authors.lastname: Arias-Londoño
authors.affiliation: Faculty of Engineering, Universidad de Antioquia, Calle 67 Número 53-108, Medellín 1226, Colombia.
authors.forename: J F
authors.initials: JF
authors.lastname: Vargas-Bonilla
authors.affiliation: Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 22254, Saudi Arabia.
authors.forename: K
authors.initials: K
authors.lastname: Daqrouq
authors.affiliation: Department of Neurology, Knappschaftskrankenhaus, Ruhr-University, In der Schornau 23-25, Bochum D-44892, Germany.
authors.forename: S
authors.initials: S
authors.lastname: Skodda
authors.affiliation: Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic.
authors.forename: J
authors.initials: J
authors.lastname: Rusz
authors.affiliation: Pattern Recognition Lab, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Martensstraβe 3, Erlangen 91058, Germany.
authors.forename: E
authors.initials: E
authors.lastname: Nöth
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D018153:Czech Republic; D005260:Female; D005858:Germany; D006801:Humans; D007802:Language; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D010700:Phonetics; D011932:Reading; D021641:Recognition, Psychology; D013030:Spain; D013060:Speech / Q000502:physiology*; D013061:Speech Acoustics