SAND Challenge: Four Approaches for Dysartria Severity Classification

2025/12/02 by Deshpande, Gauri, Battula, Harish, Panda, Ashish +1
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sound (cs.SD)

paper · doi:10.48550/arxiv.2512.02669

Abstract

This paper presents a unified study of four distinct modeling approaches for classifying dysarthria severity in the Speech Analysis for Neurodegenerative Diseases (SAND) challenge. All models tackle the same five class classification task using a common dataset of speech recordings. We investigate: (1) a ViT-OF method leveraging a Vision Transformer on spectrogram images, (2) a 1D-CNN approach using eight 1-D CNN's with majority-vote fusion, (3) a BiLSTM-OF approach using nine BiLSTM models with majority vote fusion, and (4) a Hierarchical XGBoost ensemble that combines glottal and formant features through a two stage learning framework. Each method is described, and their performances on a validation set of 53 speakers are compared. Results show that while the feature-engineered XGBoost ensemble achieves the highest macro-F1 (0.86), the deep learning models (ViT, CNN, BiLSTM) attain competitive F1-scores (0.70) and offer complementary insights into the problem.

Citations

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Glottal Closure and Opening Instant Detection from Speech Signals
• A Unified Approach to Interpreting Model Predictions

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