Title: Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation Show Chinese title

Title: 利用DSSCNet和跨语料库适应增强语音独立的构音障碍语音严重程度分类

Abstract: Dysarthric speech severity classification is crucial for objective clinical assessment and progress monitoring in individuals with motor speech disorders. Although prior methods have addressed this task, achieving robust generalization in speaker-independent (SID) scenarios remains challenging. This work introduces DSSCNet, a novel deep neural architecture that combines Convolutional, Squeeze-Excitation (SE), and Residual network, helping it extract discriminative representations of dysarthric speech from mel spectrograms. The addition of SE block selectively focuses on the important features of the dysarthric speech, thereby minimizing loss and enhancing overall model performance. We also propose a cross-corpus fine-tuning framework for severity classification, adapted from detection-based transfer learning approaches. DSSCNet is evaluated on two benchmark dysarthric speech corpora: TORGO and UA-Speech under speaker-independent evaluation protocols: One-Speaker-Per-Severity (OSPS) and Leave-One-Speaker-Out (LOSO) protocols. DSSCNet achieves accuracies of 56.84% and 62.62% under OSPS and 63.47% and 64.18% under LOSO setting on TORGO and UA-Speech respectively outperforming existing state-of-the-art methods. Upon fine-tuning, the performance improves substantially, with DSSCNet achieving up to 75.80% accuracy on TORGO and 68.25% on UA-Speech in OSPS, and up to 77.76% and 79.44%, respectively, in LOSO. These results demonstrate the effectiveness and generalizability of DSSCNet for fine-grained severity classification across diverse dysarthric speech datasets. Show Chinese abstract

Abstract: 构音障碍语音严重程度分类对于运动性言语障碍个体的客观临床评估和进展监测至关重要。尽管之前的方法已经解决了这个任务，但在说话人独立（SID）场景中实现鲁棒的泛化仍然具有挑战性。这项工作引入了DSSCNet，这是一种新颖的深度神经架构，结合了卷积、压缩-激励（SE）和残差网络，帮助它从梅尔频谱图中提取构音障碍语音的区分性表示。SE块的添加选择性地关注构音障碍语音的重要特征，从而减少损失并提高整体模型性能。我们还提出了一种基于检测的迁移学习方法适应的跨语料库微调框架用于严重程度分类。DSSCNet在两个基准构音障碍语音语料库TORGO和UA-Speech上进行了评估，采用说话人独立评估协议：每严重程度一说话人（OSPS）和留出一说话人（LOSO）协议。在OSPS下，DSSCNet在TORGO和UA-Speech上的准确率分别为56.84%和62.62%，在LOSO设置下分别为63.47%和64.18%，超过了现有的最先进方法。经过微调后，性能显著提高，DSSCNet在OSPS下在TORGO上达到最高75.80%的准确率，在UA-Speech上达到68.25%，在LOSO下分别达到最高77.76%和79.44%。这些结果证明了DSSCNet在不同构音障碍语音数据集上细粒度严重程度分类的有效性和泛化能力。