# P-vectors: A Parallel-Coupled TDNN/Transformer Network for Speaker Verification

arXiv: https://arxiv.org/abs/2305.14778 · HTML: https://mlsanity.com/paper/2305.14778/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 1 of 1)

- [GMM-ResNext: Combining Generative and Discriminative Models for Speaker Verification](https://mlsanity.com/paper/2407.03135/) (2024)

## Abstract (arXiv)

Typically, the Time-Delay Neural Network (TDNN) and Transformer can serve as a backbone for Speaker Verification (SV). Both of them have advantages and disadvantages from the perspective of global and local feature modeling. How to effectively integrate these two style features is still an open issue. In this paper, we explore a Parallel-coupled TDNN/Transformer Network (p-vectors) to replace the serial hybrid networks. The p-vectors allows TDNN and Transformer to learn the complementary information from each other through Soft Feature Alignment Interaction (SFAI) under the premise of preserving local and global features. Also, p-vectors uses the Spatial Frequency-channel Attention (SFA) to enhance the spatial interdependence modeling for input features. Finally, the outputs of dual branches of p-vectors are combined by Embedding Aggregation Layer (EAL). Experiments show that p-vectors outperforms MACCIF-TDNN and MFA-Conformer with relative improvements of 11.5% and 13.9% in EER on VoxCeleb1-O.