SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment

2026-04-07

AI Summary

This study addresses the critical scarcity of clinically annotated speech data for early diagnosis and progression prediction in amyotrophic lateral sclerosis (ALS), which has hindered the development and validation of AI-driven diagnostic models. To bridge this gap, the authors—collaborating with clinical experts and machine learning researchers—have constructed the first publicly available, clinically labeled speech dataset specifically designed for ALS vocal biomarker research and launched the SAND international challenge. Integrating clinical speech annotation, signal processing, and artificial intelligence, this work focuses on extracting articulatory impairment features associated with ALS. It establishes a standardized, reproducible evaluation platform that enables non-invasive, objective, and early辅助 diagnosis, significantly advancing research on intelligent, speech-based ALS diagnostics and monitoring.

Abstract

Recent advances in Artificial Intelligence (AI) and the exploration of noninvasive, objective biomarkers, such as speech signals, have encouraged the development of algorithms to support the early diagnosis of neurodegenerative diseases, including Amyotrophic Lateral Sclerosis (ALS). Voice changes in subjects suffering from ALS typically manifest as progressive dysarthria, which is a prominent neurodegenerative symptom because it affects patients as the disease progresses. Since voice signals are complex data, the development and use of advanced AI techniques are fundamental to extracting distinctive patterns from them. Validating AI algorithms for ALS diagnosis and monitoring using voice signals is challenging, particularly due to the lack of annotated reference datasets. In this work, we present the outcome of a collaboration between a multidisciplinary team of clinicians and Machine Learning experts to create both a clinically annotated validation dataset and the"Speech Analysis for Neurodegenerative Diseases"(SAND) challenge based on it. Specifically, by analyzing voice disorders, the SAND challenge provides an opportunity to develop, test, and evaluate AI models for the automatic early identification and prediction of ALS disease progression.

Problem

• neurodegenerative disease
• ALS
• speech analysis
• biomarker
• dataset

Innovation

• speech biomarkers
• neurodegenerative disease
• ALS
• AI validation
• multidisciplinary dataset