# NPU Speaker Verification System for INTERSPEECH 2020 Far-Field Speaker Verification Challenge

arXiv: https://arxiv.org/abs/2008.03521 · HTML: https://mlsanity.com/paper/2008.03521/

## Key facts

- Cited by 6 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Beijing ZKJ-NPU Speaker Verification System for VoxCeleb Speaker Recognition Challenge 2021](https://mlsanity.com/paper/2109.03568/) (2021)
- [Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification](https://mlsanity.com/paper/2106.09320/) (2021)
- [Whisper-SV: Adapting Whisper for Low-data-resource Speaker Verification](https://mlsanity.com/paper/2407.10048/) (2024)
- [Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification](https://mlsanity.com/paper/2112.13569/) (2021)
- [Distance-based Weight Transfer from Near-field to Far-field Speaker Verification](https://mlsanity.com/paper/2303.00264/) (2023)
- [MSV Challenge 2022: NPU-HC Speaker Verification System for Low-resource Indian Languages](https://mlsanity.com/paper/2211.16694/) (2022)

## Abstract (arXiv)

This paper describes the NPU system submitted to Interspeech 2020 Far-Field Speaker Verification Challenge (FFSVC). We particularly focus on far-field text-dependent SV from single (task1) and multiple microphone arrays (task3). The major challenges in such scenarios are short utterance and cross-channel and distance mismatch for enrollment and test. With the belief that better speaker embedding can alleviate the effects from short utterance, we introduce a new speaker embedding architecture - ResNet-BAM, which integrates a bottleneck attention module with ResNet as a simple and efficient way to further improve the representation power of ResNet. This contribution brings up to 1% EER reduction. We further address the mismatch problem in three directions. First, domain adversarial training, which aims to learn domain-invariant features, can yield to 0.8% EER reduction. Second, front-end signal processing, including WPE and beamforming, has no obvious contribution, but together with data selection and domain adversarial training, can further contribute to 0.5% EER reduction. Finally, data augmentation, which works with a specifically-designed data selection strategy, can lead to 2% EER reduction. Together with the above contributions, in the middle challenge results, our single submission system (without multi-system fusion) achieves the first and second place on task 1 and task 3, respectively.