Rusz Jan, Novotny Michal, Hlavnicka Jan, Tykalova Tereza, Ruzicka Evzen
IEEE Trans Neural Syst Rehabil Eng. 2017 Aug;25(8):1319-1321. doi: 10.1109/TNSRE.2016.2621885. Epub 2016 Oct 26.
Recently, based on voice cepstral analysis, Benba et al. (IEEE T. Neur. Sys. Reh., vol. 24, pp. 1100-1108, 2016) have reported discrimination between patients with Parkinson's disease and different neurological disorders with high classification accuracy up to 90%. Using the same approach, we were able to experimentally separate two groups of normal healthy speakers with 96% classification accuracy and showed that the method proposed by Benba et al. may not be appropriate for discrimination between different neurological diseases. In particular, voice cepstral analysis appears to be sensitive to specific speakers' characteristics such as gender or age. Our findings emphasize several assumptions that can be considered as basic necessary conditions for research reporting speech data in progressive neurodegenerative diseases.
最近，基于语音倒谱分析，本巴等人（《IEEE神经系统康复学汇刊》，第24卷，第1100 - 1108页，2016年）报告称，在区分帕金森病患者和不同神经系统疾病患者方面，分类准确率高达90%。采用相同的方法，我们能够通过实验以96%的分类准确率区分两组正常健康的说话者，并表明本巴等人提出的方法可能不适用于区分不同的神经系统疾病。特别是，语音倒谱分析似乎对特定说话者的特征（如性别或年龄）敏感。我们的研究结果强调了几个假设，这些假设可被视为在进行性神经退行性疾病中报告语音数据的研究的基本必要条件。
