DS-TDNN: Dual-Stream Time-Delay Neural Network With Global-Aware Filter for Speaker Verification
Yangfu Li, Jiapan Gan, Xiaodan Lin, Yingqiang Qiu, Hongjian Zhan, Hui Tian
Abstract
Conventional time-delay neural networks (TDNNs) struggle to handle long-range context, their ability to represent speaker information is therefore limited for long utterances. Existing solutions either depend on increasing model complexity or try to strike a balance between local features and global context to address this issue. To effectively leverage the long-term dependencies of audio signals and constrain model complexity, we introduce a novel module called Global-aware Filter layer (GF layer) in this work, which employs a set of learnable transform-domain filters between a 1D discrete Fourier transform and its inverse transform to capture global context. Additionally, we develop a dynamic filtering strategy and a sparse regularization method to enhance the performance of the GF layer and prevent overfitting. Based on the GF layer, we present a dual-stream TDNN architecture called DS-TDNN for automatic speaker verification (ASV), which utilizes two unique branches to extract both local and global features in parallel and employs an efficient strategy to fuse different-scale information. Experiments on the Voxceleb and SITW databases demonstrate that the DS-TDNN achieves a relative improvement of 10% together with a relative decline of 20% in computational cost over the ECAPA-TDNN in the speaker verification task. This improvement becomes more evident as the utterance's duration grows. Furthermore, the DS-TDNN also beats popular deep residual models and attention-based systems on utterances of arbitrary length.