Abstract
This paper presents preliminary results for the analysis of intelligibility in the speech of Parkinson’s Disease (PD) patients. An automatic speech recognition system is used to compute the word error rate (WER), the Levenshtein distance, and the similitude based dynamic time warping. The corpus of the speech recognizer is formed with speech recordings of three Diadochokinetic speech tasks: /pa-ta-ka/, /pa-ka-ta/, and /pe-ta-ka/. The data consist of 50 PD patients and 50 Healthy Controls. According to the results, the recognition error is lower for the healthy speakers (WER = $$2.70\%$$ ) respect to the PD patients (WER = $$11.3\%$$ ).
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Parra-Gallego, L. F., Arias-Vergara, T., Vásquez-Correa, J. C., Garcia-Ospina, N., Orozco-Arroyave, J. R., & Nöth, E. (2018). Automatic Intelligibility Assessment of Parkinson’s Disease with Diadochokinetic Exercises. In Communications in Computer and Information Science (Vol. 916, pp. 223–230). Springer Verlag. https://doi.org/10.1007/978-3-030-00353-1_20
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