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arXiv · 2006.00452

Crossed-Time Delay Neural Network for Speaker Recognition

Liang Chen·Yanchun Liang·Xiaohu Shi·You Zhou·Chunguo Wu
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Abstract

Time Delay Neural Network (TDNN) is a well-performing structure for DNN-based speaker recognition systems. In this paper we introduce a novel structure Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN. Inspired by the multi-filters setting of convolution layer from convolution neural network, we set multiple time delay units each with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, FTDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition and can better handle training of a larger batch in a shorter training time, which better utilize the calculation resources. The code of the new model is released at https://github.com/chenllliang/CTDNN

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BibTeXRIS

Liang Chen, Yanchun Liang, Xiaohu Shi, You Zhou, Chunguo Wu. 2021-12-07. Crossed-Time Delay Neural Network for Speaker Recognition. https://arxiv.org/abs/2006.00452

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