Backend Ensemble for Speaker Verification and Spoofing Countermeasure
Li Zhang, Yue Li, Huan Zhao, Qing Wang, Lei Xie

TL;DR
This paper presents a novel backend ensemble approach for speaker verification and spoofing detection, utilizing circulant matrix transformations, convolutional fusion, and attention mechanisms to improve performance in SASV tasks.
Contribution
It introduces new embedding transformation and fusion techniques, including circulant matrix stacking and attention-based neural networks, for enhanced speaker verification and anti-spoofing.
Findings
Achieved state-of-the-art SASV-EER of 0.559%
Demonstrated effectiveness of circulant embedding and attention mechanisms
System ranked fifth in the challenge
Abstract
This paper describes the NPU system submitted to Spoofing Aware Speaker Verification Challenge 2022. We particularly focus on the \textit{backend ensemble} for speaker verification and spoofing countermeasure from three aspects. Firstly, besides simple concatenation, we propose circulant matrix transformation and stacking for speaker embeddings and countermeasure embeddings. With the stacking operation of newly-defined circulant embeddings, we almost explore all the possible interactions between speaker embeddings and countermeasure embeddings. Secondly, we attempt different convolution neural networks to selectively fuse the embeddings' salient regions into channels with convolution kernels. Finally, we design parallel attention in 1D convolution neural networks to learn the global correlation in channel dimensions as well as to learn the important parts in feature dimensions.…
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Taxonomy
TopicsSpeech Recognition and Synthesis · Speech and Audio Processing · Music and Audio Processing
