Jorge A. Gómez-García

The advancement of Parkinson’s Disease (PD) diagnosis through speech analysis is hindered by a notable lack of publicly available, diverse language datasets, limiting the reproducibility and further exploration of existing research. In response to this gap, we introduce a comprehensive corpus from 108 native Castilian Spanish speakers, comprising 55 healthy controls and 53 individuals diagnosed with PD, all of whom were under pharmacological treatment and recorded in their medication-optimized state. This unique dataset features a wide array of speech tasks, including sustained phonation of the five Spanish vowels, diadochokinetic tests, 16 listen-and-repeat utterances, and free monologues. The dataset emphasizes accuracy and reliability through specialist manual transcriptions of the listen-and-repeat tasks and utilizes Whisper for automated monologue transcriptions, making it the most complete public corpus of Parkinsonian speech, and the first in Castillian Spanish. NeuroVoz is composed by 2,903 audio recordings averaging 26.88 \pm 3.35 recordings per participant, offering a substantial resource for the scientific exploration of PD’s impact on speech. This dataset has already underpinned several studies, achieving a benchmark accuracy of 89% in PD speech pattern identification, indicating marked speech alterations attributable to PD. Despite these advances, the broader challenge of conducting a language-agnostic, cross-corpora analysis of Parkinsonian speech patterns remains an open area for future research. This contribution not only fills a critical void in PD speech analysis resources but also sets a new standard for the global research community in leveraging speech as a diagnostic tool for neurodegenerative diseases.

This is the third of a three-part series devoted to review the current state of the art of automatic voice condition analysis systems. A direct continuation to “On the design of automatic voice condition analysis systems. Part I: review of concepts and an insight to the state of the art” and to “On the design of automatic voice condition analysis systems. Part II: review of speaker recognition techniques and study on the effects of different variability factors” already published in this journal. The goal of this paper is to compile the most significant parameterisation approaches used in the literature for automatic voice condition analysis systems, along with a critical discussion about their usefulness, providing the user with a comprehensive review of the most important techniques used for acoustic modelling in the field. The paper presents the mathematical formulation and physical interpretation of a series of perturbation and fluctuation parameters, noise features, complexity based parameters, modulation spectra, morphological parameters, and spectral-cepstral coefficients; and is complemented with a library written in MATLAB®, which has been made available to the readers in an online software repository.

Parkinson’s Disease (PD) affects speech in the form of dysphonia and hypokinetic dysarthria. Multiple studies have evaluated PD’s influence on different aspects of speech, showing differences between speakers with and without PD. Most recent studies are focused on the proposal of new automatic and objective tools to help in the diagnosis and severity assessment. This comprehensive review identifies the most common features and machine learning techniques employed in automatically detecting and assessing the severity of PD using phonatory and articulatory aspects of speech and voice. We discuss their discriminant properties and literature findings as well as identify common methodological issues that can potentially bias results. The objective is to provide a broad overview of these methods, their advantages and disadvantages, and to identify the most promising methodologies to be explored in future works. We conclude that there is clear evidence that the articulatory and phonatory aspects of speech and voice are relevant for the automatic detection and severity assessment of PD. However, there is no standard methodology sufficiently validated in a clinical trial, and further research is required, especially to develop larger corpora and identify new objective biomarkers.