NeuroVoz: A Comprehensive Spanish Corpus for Parkinsonian Speech Analysis

מושגי ליבה

The author introduces NeuroVoz, a comprehensive corpus of Castilian Spanish speakers with Parkinson's disease, aiming to address the lack of diverse language datasets for PD diagnosis through speech analysis.

תקציר

NeuroVoz is a unique dataset featuring various speech tasks from 108 native Castilian Spanish speakers diagnosed with PD. It includes sustained phonation, diadochokinetic tests, listen-and-repeat utterances, and free monologues. The dataset emphasizes accuracy and reliability in analyzing PD's impact on speech patterns. Several studies have utilized this dataset to achieve high accuracy in identifying PD speech patterns. The dataset fills a critical void in PD speech analysis resources and sets a new standard for leveraging speech as a diagnostic tool for neurodegenerative diseases.

סטטיסטיקה

NeuroVoz comprises 2,903 audio recordings.
Benchmark accuracy achieved is 89% in identifying PD speech patterns.
The dataset contains 108 Spanish Castilian speakers.
Average of 26.88 ± 3.35 audio recordings per participant.

ציטוטים

"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."

תובנות מפתח מזוקקות מ:

NeuroVoz

by Jana... ב- arxiv.org 03-06-2024

https://arxiv.org/pdf/2403.02371.pdf

שאלות מעמיקות

How can the NeuroVoz dataset be utilized to improve diagnostic tools beyond Parkinson's disease?

The NeuroVoz dataset, with its comprehensive collection of speech recordings from individuals diagnosed with Parkinson's Disease (PD) and healthy controls, offers a valuable resource for advancing diagnostic tools in various neurodegenerative diseases. By leveraging machine learning algorithms on this dataset, researchers can explore patterns and markers in speech that may extend beyond PD. For instance, the dataset could be used to identify commonalities or unique characteristics in speech patterns across different neurodegenerative conditions. This comparative analysis could lead to the development of more nuanced diagnostic tools capable of distinguishing between various disorders based on subtle differences in speech features.
Furthermore, the diverse range of tasks included in the NeuroVoz corpus, such as sustained phonation of vowels, diadochokinetic tests, listen-and-repeat utterances, and free monologues, provides a rich source of data for training models to detect abnormalities not only related to motor symptoms but also cognitive impairments or mood disturbances present in other neurological conditions. By expanding research beyond PD-specific markers and incorporating a broader spectrum of neurodegenerative diseases into the analysis pipeline using this dataset as a foundation, it is possible to enhance diagnostic accuracy and early detection capabilities across multiple disorders.

What are potential limitations or biases that could affect the accuracy of using speech analysis as a diagnostic tool?

While utilizing speech analysis for diagnostics presents promising opportunities, there are several limitations and biases that need consideration to ensure accurate results:

Sample Bias: The composition of participants within datasets like NeuroVoz may not fully represent all demographic groups affected by neurological diseases. Biases towards certain age ranges or genders could impact model generalizability.

Data Quality: Variations in recording equipment quality or environmental factors during data collection can introduce noise into the dataset leading to inaccuracies during analysis.

Disease Progression: Speech patterns may evolve over time due to disease progression which might result in misdiagnosis if not accounted for when analyzing longitudinal data.

Language Variability: Differences in dialects or accents among speakers can influence acoustic features extracted from speech signals affecting model performance especially when applied cross-culturally.

Addressing these limitations requires robust validation strategies including diverse participant recruitment methods ensuring representative samples along with meticulous data preprocessing techniques aimed at minimizing bias introduced by external factors.

How might advancements in ML and AI impact future development similar corpora for medical research?

Advancements in Machine Learning (ML) and Artificial Intelligence (AI) offer significant potential for enhancing future developments similar corpora for medical research:

Automated Data Annotation: ML algorithms can streamline manual transcription processes reducing human effort while maintaining high accuracy levels essential for large-scale datasets like those found within medical research corpora.

Feature Extraction Optimization: AI techniques enable efficient extraction of complex voice quality features aiding researchers uncover subtle variations indicative of specific health conditions improving diagnosis precision.

Cross-Corpus Analysis ML models facilitate cross-corpus comparisons enabling researchers access larger datasets amalgamating information from multiple sources enriching insights drawn from analyses fostering collaborative efforts within medical communities globally.

By harnessing these technological advancements effectively within corpus development frameworks researchers stand poised revolutionize healthcare practices through innovative diagnostics methodologies grounded upon robust empirical evidence derived from extensive datasets facilitated by ML-driven approaches tailored specifically towards medical applications