SAND: The Challenge on Speech Analysis for Neurodegenerative Disease Assessment
Authors: Giovanna Sannino, Ivanoe De Falco, Nadia Brancati, Laura Verde, Maria Frucci, Daniel Riccio, Vincenzo Bevilacqua, Antonio Di Marino, Lucia Aruta, Valentina Virginia Iuzzolino, Gianmaria Senerchia, Myriam Spisto, Raffaele Dubbioso
Organizations: National Research Council of Italy (CNR), Institute for High-Performance Computing and Networking (ICAR), Naples, 80131, Italy · Department of Mathematics and Physics, University of Campania “Luigi Vanvitelli”, Caserta, 81100, Italy · Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, Naples, 80125, Italy · Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, Naples, 80131, Italy · Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, 80131, Italy · Department of Psychology, University of Campania “Luigi Vanvitelli”, Caserta, 81100, Italy
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.
Figures & tables
Figure 1: Stylized illustration of the Vox4Health acquisition setup. The smartphone is placed at approximately 20 cm from the subject’s mouth with an angle of about 45 ∘ . Audio is recorded via the Vox4Health m-health application and stored as 8 kHz, 16-bit WAV files.
Class
Severity Level
Distribution (%)
Class 1
ALS – Severe
2.23%
Class 2
ALS – Moderate
9.55%
Class 3
ALS – Mild
20.95%
Class 4
ALS – No Dysarthria
27.94%
Class 5
Healthy
39.33%
Table 1: Class distribution for Task 1.
Class
Severity Level
Distribution (%)
Class 1
ALS – Severe
13.64%
Class 2
ALS – Moderate
21.97%
Class 3
ALS – Mild
28.79%
Class 4
ALS – No Dysarthria
35.60%
Table 2: Class distribution for Task 2.
Figure 2: Number of teams registered for the SAND Challenge for each continent.
Figure 3: Number of Teams registered for the SAND Challenge for each country.
Figure 4: Number of Teams Participating for each country in a) Task 1, and b) Task 2.
Amyotrophic lateral sclerosis (ALS) progressively impairs speech motor control, making acoustic analysis a promising biomarker for severity and progression estimation. We propose a subject-level graph framework that aggregates multiple phonation recordings into a unique k-nearest-neighbor graph built from pretrained SSL embeddings of 2s segments. We compare four SSL front-ends (wav2vec 2.0, HuBERT, data2vec-audio, and UniSpeech-SAT) and five graph neural networks (GCN, residual GCN, GAT, GraphSAGE, and GIN) on the SAND dataset tasks (339 participants: 205 ALS, 134 control): 5-class dysarthria severity and 4-class ALSFRS-R progression prediction. On the official validation set, the best configuration (HuBERT+GIN) achieves macro-F1 of 0.73 for Task 1 and 0.69 for Task 2, outperforming SAND validation baselines (0.61 and 0.58). These results highlight the potential of combining GNNs with pretrained cross-lingual speech representations for low-resource ALS detection and progression monitoring.
Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data. This paper presents NeurMLLM, an efficient multimodal generative framework for neurodegenerative disease staging. NeurMLLM first encodes the spectrograms and Mel-frequency cepstral coefficients of audio data with vision transformers and projects their representations into the embedding space of a large language model (LLM), where they are concatenated with transcript and demographic instruction tokens as a single unified sequence. The LLM is then instruction-tuned via Low-Rank Adaptation using task prompts to autoregressively predict a constrained label token, enabling a generative classification. By evaluating on the Bridge2AI-Voice dataset for fine-grained staging of AD and PD, we observe that NeurMLLM achieves strong performance, consistently outperforming classical machine learning methods and existing LLM-based approaches. The results show the high potential of multimodal LLMs in neurodegenerative disease staging, improving staging accuracy and supporting accessible deployment.
Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong
Dept. of Information Systems and Cybersecurity The University of Texas at San Antonio San Antonio, TX, USA · School of Engineering Medicine Texas A&M University Houston, TX, USA
Automatic speech recognition (ASR) has advanced remarkably for standard speech, yet speech affected by neurological conditions remains a challenge. We present S-DiverSe (Spanish Diverse Speech), a corpus of 3.2 hours of in-the-wild Spanish speech from 22 speakers with amyotrophic lateral sclerosis, Parkinson's disease, and stroke. The dataset contains 444 manually transcribed audio segments with metadata on speaker sex, disease type, and intelligibility. S-DiverSe is designed to support ASR evaluation and development for neurologically affected Spanish speech. We describe the dataset, analyze its composition, and report baseline ASR results alongside initial adaptation experiments. Our findings reveal that heuristic text post-processing is more robust than fine-tuning for out-of-domain neurological Spanish speech. This underscores the need for dedicated in-the-wild Spanish benchmarks.
Fernando López, Fernando Ibañez, Ana Martínez +4
Scientific Research, Telefónica Innovación Digital, Spain · Universidad Autónoma de Madrid, Spain · Brno University of Technology, Czech Republic