Abstract
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 (7–15% improvement). This result was observed for both recording types (high-quality microphone and telephone).
Highlights
Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease and affects approximately seven million people worldwide
Performances were measured with the Equal Error Rate (EER), i.e., the error rate corresponding to the threshold for which false positive ratio is equal to false negative ratio, TABLE 6 | PD vs. healthy controls (HC) classification EER obtained with different classifiers: Mel-Frequency Cepstral Coefficients (MFCCs)-Gaussian Mixture Model (GMM) baseline, and x-vectors combined either with cosine similarity or with Probabilistic Linear Discriminant Analysis (PLDA), with and without data augmentation
We compared the efficacy of this method with the more classical MFCC-GMM technique, and varied several experimental and methodological aspects to determine the optimal approach
Summary
Parkinson’s disease (PD) is the second most common neurodegenerative disease after Alzheimer’s disease and affects approximately seven million people worldwide. Its prevalence in industrialized countries is around 0.3% and increases with age: 1% of people over the age of 60 and up to 4% of those over 80 are affected (De Lau and Breteler, 2006). Parkinson Detection Using X-Vectors and environmental factors This disease results in motor disorders worsening over time caused by a progressive loss of dopaminergic neurons in the substantia nigra (located in the midbrain). The diagnosis is made when at least two of the following three symptoms are noted: bradykinesia (slowness of movement), rigidity, and tremors at rest These motor symptoms appear once 50–60% of dopaminergic neurons in the substantia nigra (Haas et al, 2012) and 60–80% of their striatal endings (Fearnley and Lees, 1991) have degenerated. That is why detecting PD in the early stages remains a big challenge, in order to test treatments before the occurrence of large irreversible brain damage, and later to slow down, or even stop, its progression from the beginning
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