Abstract
The impact of speech compression in the automatic classification of speakers with Parkinson’s disease (PD) and healthy controls (HC) is tested. The set of codecs considered to compress the speech recordings includes G.722, G.226, GSM-EFR, AMR-WB, SILK, and Opus. A total of 100 speakers (50 with PD and 50 HC) are asked to read a text with 36 words. The recordings are compressed from bit-rates of 705.6 kbps down to 6.6 kbps. The method addressed to discriminate between speakers with PD and HC consists on the systematic segmentat ion of voiced and unvoiced speech frames. Each kind of frame is characterized independently. For voiced segments noise, perturbation, and cepstral features are considered. The unvoiced segments are characterized with Bark band energies and cepstral features. According to the results the codecs evaluated in this paper do not affect significantly the accuracy of the system, indicating that the addressed methodology could be used for the telemonitoring of PD patients through Internet or through the mobile communications network.
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Orozco-Arroyave, J.R., García, N., Vargas-Bonilla, J.F., Nöth, E. (2015). Automatic Detection of Parkinson’s Disease from Compressed Speech Recordings. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_10
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DOI: https://doi.org/10.1007/978-3-319-24033-6_10
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