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.
Abstract (translated)
构音障碍语音严重程度分类对于患有运动性言语障碍的个体来说，是临床客观评估和进程监测的关键。尽管之前的方法已经解决了这一任务，但在说话人无关（SID）场景中实现稳健泛化仍然具有挑战性。本文介绍了一种新颖的深度神经网络架构DSSCNet，该架构结合了卷积、挤压-激励（SE）以及残差网络，有助于从梅尔频谱图中提取构音障碍语音的区分表示。通过添加SE模块，可以有针对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。我们还提出了一种用于严重程度分类的跨语料库微调框架，该框架借鉴了基于检测的迁移学习方法。 DSSCNet在两个基准构音障碍语音数据集（TORGO和UA-Speech）上进行了评估，在说话人无关的评估协议下：单个说话人每一种严重程度（OSPS）和留一说话人法（LOSO）。在TORGO和UA-Speech数据集中，DSSCNet分别达到了56.84%和62.62%（OSPS）以及63.47%和64.18%（LOSO）的准确率，在这些任务上优于现有的最先进方法。经过微调后，性能显著提高：在TORGO数据集中DSSCNet分别达到了75.80%（OSPS）和77.76%（LOSO），而在UA-Speech数据集中则为68.25%（OSPS）和79.44%（LOSO）。这些结果证明了DSSCNet在跨多种构音障碍语音数据集进行精细程度分类时的有效性和泛化能力。
URL
https://arxiv.org/abs/2509.13442
