Crossed-Time Delay Neural Network for Speaker Recognition

• URL: http://arxiv.org/abs/2006.00452v3
• Date: Tue, 7 Dec 2021 06:22:23 GMT
• Title: Crossed-Time Delay Neural Network for Speaker Recognition
• Authors: Liang Chen and Yanchun Liang and Xiaohu Shi and You Zhou and Chunguo Wu
• Abstract summary: We introduce a novel structure Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks.
• Score: 5.216353911330589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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