CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification

CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification

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Self-supervised learning (SSL) models for speaker verifica- tion (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 pro- pose 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.

Self-supervised learning (SSL) models for speaker verifica- tion (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 pro- pose 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.

Self-supervised learning, speaker verification, speaker extractor, pooling mechanism, speech classification

Self-supervised learning, speaker verification, speaker extractor, pooling mechanism, speech classification

PENG, J.; MOŠNER, L.; ZHANG, L.; PLCHOT, O.; STAFYLAKIS, T.; BURGET, L.; ČERNOCKÝ, J.

06.04.2025

IEEE Signal Processing Society

Hyderabad

979-8-3503-6874-1

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10889058

CA-MHFA_A_Context-Aware_Multi-Head_Factorized_Attentive_Pooling_for_SSL-Based_Speaker_Verification