Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

• Authors: MUCHA J.; GALAZ Z.; MEKYSKA J.; KISKA T.; ZVONCAK V.; SMEKAL Z.; ELIÁŠOVÁ Ilona; MRAČKOVÁ Martina; KOŠŤÁLOVÁ Milena; REKTOROVÁ Irena; FAUNDEZ-ZANUY M.; ALONSO-HERNANDEZ JB.
• Year of publication: 2017
• Type: Article in Proceedings
• Conference: 40th International Conference on Telecommunications and Signal Processing, TSP 2017
• MU Faculty or unit: Central European Institute of Technology
• Doi: http://dx.doi.org/10.1109/TSP.2017.8076086
• Keywords: acoustic analysis; binary classification; hypokinetic dysarthria; Parkinson’s disease; poem recitation
• Description: Up to 90% of patients with Parkinson’s disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In case of univariate classification analysis, sensitivity of 62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.

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