对比四种方法，用语音数据分类帕金森病导致的言语障碍严重程度。
SAND Challenge: Four Approaches for Dysartria Severity Classification
- 用视觉变压器、一维卷积、双向LSTM和集成学习分别建模语音特征。
- 基于声门与共振峰特征的集成模型在验证集上达到0.86的宏平均F1最优。
- 深度学习模型虽性能稍逊，但提供可解释性洞察，适合临床辅助诊断。
本文对四种不同的建模方法在语音分析神经退行性疾病（SAND）挑战赛中用于分类言语障碍严重程度进行了统一研究。所有模型均使用相同的语音录音数据集完成五分类任务。我们评估了：(1) 基于频谱图图像的视觉变压器（ViT-OF）方法；(2) 八个一维卷积神经网络结合多数投票融合的1D-CNN方法；(3) 九个双向LSTM模型通过多数投票融合的BiLSTM-OF方法；(4) 两级学习框架下结合声门与共振峰特征的分层XGBoost集成模型。每种方法均有详细描述，并在包含53名说话人的验证集上进行性能比较。结果表明，尽管基于特征工程的XGBoost集成模型取得最高宏平均F1为0.86，深度学习模型（ViT、CNN、BiLSTM）也实现了0.70的可观F1分数，并提供了互补性的问题理解视角。
原文摘要 · Abstract (English)
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.
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