NPU Speaker Verification System For INTERSPEECH 2020 Far-field Speaker Verification Challenge

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

Stats

• citations9
• S2 citations—
• github stars0
• HF likes0
• heat score7.50
• arxiv keyzhang2020npu

Related papers

• The HCCL Speaker Verification System For Far-field Speaker Verification Challenge (2021)0.00
• Deep Speaker Embeddings For Far-field Speaker Recognition On Short Utterances (2020)11.29
• How To Leverage Dnn-based Speech Enhancement For Multi-channel Speaker Verification? (2022)0.00
• Analyzing Speaker Verification Embedding Extractors And Back-ends Under Language And Channel Mismatch (2022)0.00
• Multi-level Transfer Learning From Near-field To Far-field Speaker Verification (2021)0.00
• Parameterized Channel Normalization For Far-field Deep Speaker Verification (2021)3.58
• STC Speaker Recognition Systems For The Voices From A Distance Challenge (2019)7.81
• Single Channel Far Field Feature Enhancement For Speaker Verification In The Wild (2020)0.00