SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment

TL;DR

The paper introduces the SAND challenge, a collaborative effort to develop and evaluate AI models for early ALS detection and progression monitoring using speech signals, supported by a new annotated dataset.

Contribution

It presents a new clinically annotated dataset and a challenge framework to advance AI methods for neurodegenerative disease assessment via speech analysis.

Findings

Created a validated speech dataset for ALS

Established a challenge to benchmark AI models

Facilitated early diagnosis and progression prediction of ALS

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.