CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification
<|reference_start|>CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification: Self-supervised learning (SSL) models for speaker verification (SV) have gained significant attention in recent years. However, existing SSL-based SV systems often struggle to capture local temporal dependencies and generalize across different tasks. In this paper, we propose context-aware multi-head factorized attentive pooling (CA-MHFA), a lightweight framework that incorporates contextual information from surrounding frames. CA-MHFA leverages grouped, learnable queries to effectively model contextual dependencies while maintaining efficiency by sharing keys and values across groups. Experimental results on the VoxCeleb dataset show that CA-MHFA achieves EERs of 0.42\%, 0.48\%, and 0.96\% on Vox1-O, Vox1-E, and Vox1-H, respectively, outperforming complex models like WavLM-TDNN with fewer parameters and faster convergence. Additionally, CA-MHFA demonstrates strong generalization across multiple SSL models and tasks, including emotion recognition and anti-spoofing, highlighting its robustness and versatility.<|reference_end|>
@article{peng2024ca-mhfa:,
    title={CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for
  SSL-Based Speaker Verification},
    author={Junyi Peng, Ladislav Mov{s}ner, Lin Zhang, Oldv{r}ich Plchot, Themos
  Stafylakis, Luk'av{s} Burget, Jan v{C}ernock'y},
    journal={arXiv preprint arXiv:2409.15234},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15234},
    primaryClass={eess.AS cs.SD}
}