MSDFEN: Multi-scale dynamic feature extraction network for pathological voice detection

Abstract

With the rapid development of deep learning methods, their application in pathological voice detection has become increasingly extensive, yielding promising results. However, most deep learning methods used in pathological voice detection employ network architectures where all constituent modules are static, performing the same operations on all inputs. This significantly limits the model’s adaptive capacity and generalization ability and restricts further improvement in model performance. To address this issue, this paper proposes a novel pathological voice detection system called the Multi-Scale Dynamic Feature Extraction Network (MSDFEN), designed to enhance the performance and adaptive capability of pathological voice detection systems. In the MSDFEN model, sinc filter banks combined with a channel attention mechanism were employed for the preprocessing of vocal signals, effectively capturing the high-frequency transitions characteristic of pathological voices. Furthermore, dynamic blocks, consisting of multiple dynamic components, were designed and integrated into a multi-scale convolutional neural network, significantly enhancing the network’s dynamic performance and enriching the features obtained through multi-scale fusion. Comparative experiments and ablation studies were conducted using three internationally recognized pathological voice detection databases: MEEI, SVD, and HUPA. the proposed model achieved recognition accuracies of 0.9883, 0.7424, and 0.8409 in these databases, and other parameters also yielded satisfactory results. Experimental results indicate that the proposed method exhibits excellent adaptive and generalization capabilities in pathological voice detection.