Fusing Time-Frequency Heterogeneous Features With Cross-Attention Mechanism for Pathological Voice Detection.

To address the critical challenges of data scarcity, feature homogenization, and limited model generalization in current pathological voice diagnosis systems, a novel algorithm was developed to integrate time-frequency heterogeneous acoustic features for multi-class pathological voice detection. The Wav2vec2-XLSR model pretrained through self-supervised learning was first employed to extract deep contextual features from time-domain voice signals. Mel-Frequency Cepstral Coefficients (MFCC) features from the frequency domain were subsequently integrated to construct a heterogeneous vocal feature space. A cross-attention mechanism from the Transformer architecture was innovatively applied to achieve dynamic spatiotemporal alignment and semantic interaction within the heterogeneous feature space, enabling complementary feature enhancement. A dual-granularity joint analysis framework encompassing vowel and sentence hierarchies was ultimately established for efficient multi-type pathological voice detection. Experimental results demonstrated that the proposed algorithm achieved 95.1% accuracy, 100% recall, 0.92 F1-score, and 0.97 area under the ROC curve (AUC) value on sentence-level Saarbruecken Voice Database (SVD) dataset. For vowel-level classification tasks, classification accuracies of 100% and 99.6% were obtained on the Massachusetts Eye and Ear Infirmary (MEEI) and SVD datasets, respectively. Multi-corpus evaluation experiments confirm the algorithm's robustness and generalization capability across different data distributions.