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.

Department of Circuit Theory Faculty of Electrical Engineering Czech Technical University Prague Technicka 2 166 27 Prague 6 Czech Republic

Department of Electrical and Computer Engineering King Abdulaziz University Jeddah 22254 Saudi Arabia

Department of Neurology Knappschaftskrankenhaus Ruhr University In der Schornau 23 25 Bochum D 44892 Germany

Faculty of Engineering Universidad de Antioquia Calle 67 Número 53 108 Medellín 1226 Colombia

Pattern Recognition Lab Friedrich Alexander Universität Erlangen Nürnberg Martensstraβe 3 Erlangen 91058 Germany

References provided by Crossref.org

Speech Biomarkers in Rapid Eye Movement Sleep Behavior Disorder and Parkinson Disease

Speech disorders in Parkinson's disease: early diagnostics and effects of medication and brain stimulation