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

TL;DR

This paper presents a speaker verification system for the INTERSPEECH 2020 challenge, introducing a new ResNet-BAM embedding architecture and various techniques to improve accuracy in far-field scenarios with short utterances and channel mismatch.

Contribution

The paper introduces ResNet-BAM, a novel speaker embedding architecture, and explores domain adversarial training, signal processing, and data augmentation to enhance far-field speaker verification.

Findings

ResNet-BAM reduces EER by up to 1%.

Domain adversarial training reduces EER by 0.8%.

Data augmentation with data selection reduces EER by 2%.

Abstract

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.