Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients - Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7 to 15 % improvement). This result was observed for both recording types (high-quality microphone and telephone).

中文翻译：

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X矢量：语音从早期帕金森氏病检测新的定量生物标志物。

许多文章使用语音分析来检测帕金森氏病（PD），但很少有文章关注该病的早期阶段和性别效应。在本文中，我们改编了称为x-vector的最新说话人识别系统，以便在早期使用语音分析检测PD。X向量是从深度神经网络（DNN）中提取的嵌入，当使用大量训练数据时，它们可以提供可靠的说话者表示并改善说话者识别能力。我们的目标是评估在早期PD检测的情况下，该技术是否会胜过更标准的分类器MFCC-GMM（梅尔频率倒谱系数-高斯混合模型），如果是，则在何种条件下胜过该技术。我们用高质量的麦克风和通过电话网络录制了221名法语使用者（最近被诊断为PD受试者和健康对照者）。分别对男人和女人进行了分析，以建立更精确的模型并评估可能的性别影响。测试了几个实验和方法论方面，以分析它们对分类性能的影响。我们评估了音频片段持续时间，数据扩充，用于神经网络训练的数据集类型，语音任务的种类以及后端分析的影响。对于与文本无关的任务，X向量技术提供了比MFCC-GMM更好的分类性能，并且似乎特别适合女性中PD的早期检测（提高了7％至15％）。