Channel attention concatenation multi-taper Fbank features for deep speaker verification

Yifan He; Liyun Xu

doi:10.1117/12.3049499

8 November 2024 Channel attention concatenation multi-taper Fbank features for deep speaker verification

Yifan He, Liyun Xu

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162J (2024) https://doi.org/10.1117/12.3049499
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Automatic speaker verification (ASV) has become a widely used application of deep learning. However, many early positive findings were rooted in traditional methods like Gaussian mixture models (GMM) rather than deep learning. While the multi-taper spectrum estimator has proven effective in enhancing GMM-based ASV accuracy, the integration of traditional multi-tapers with modern deep learning models may not be seamless. To address this, we introduce the Channel Attention Concatenation Multi-taper Fbank (CCM-Fbank), which seamlessly integrates multi-taper spectral estimation with the popular ECAPA-TDNN model, resulting in improved accuracy and robustness. Additionally, we propose a deeper model named Double Block ECAPA-TDNN, which has just over half the number of parameters of ECAPA-TDNN (C=1024), and performs better with limited training samples.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yifan He and Liyun Xu "Channel attention concatenation multi-taper Fbank features for deep speaker verification", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162J (8 November 2024); https://doi.org/10.1117/12.3049499

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