Poformer: A simple pooling transformer for speaker verification
Yufeng Ma, Yiwei Ding, Miao Zhao, Yu Zheng, Min Liu, Minqiang Xu

TL;DR
PoFormer introduces a transformer-based pooling layer for speaker verification that captures temporal information more effectively, leading to significant improvements in verification accuracy over existing pooling methods.
Contribution
The paper proposes PoFormer, a novel transformer-based pooling structure with modifications for stability, enhancing speaker embedding quality in verification tasks.
Findings
At least 13.00% improvement in EER
At least 9.12% improvement in minDCF
Outperforms existing pooling systems on various datasets
Abstract
Most recent speaker verification systems are based on extracting speaker embeddings using a deep neural network. The pooling layer in the network aims to aggregate frame-level features extracted by the backbone. In this paper, we propose a new transformer based pooling structure called PoFormer to enhance the ability of the pooling layer to capture information along the whole time axis. Different from previous works that apply attention mechanism in a simple way or implement the multi-head mechanism in serial instead of in parallel, PoFormer follows the initial transformer structure with some minor modifications like a positional encoding generator, drop path and LayerScale to make the training procedure more stable and to prevent overfitting. Evaluated on various datasets, PoFormer outperforms the existing pooling system with at least a 13.00% improvement in EER and a 9.12% improvement…
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Taxonomy
TopicsSpeech Recognition and Synthesis · Music and Audio Processing · Speech and Audio Processing
MethodsLayerScale
