The NUS-HLT System for ICASSP2024 ICMC-ASR Grand Challenge
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Abstract (arXiv)
This paper summarizes our team's efforts in both tracks of the ICMC-ASR Challenge for in-car multi-channel automatic speech recognition. Our submitted systems for ICMC-ASR Challenge include the multi-channel front-end enhancement and diarization, training data augmentation, speech recognition modeling with multi-channel branches. Tested on the offical Eval1 and Eval2 set, our best system achieves a relative 34.3% improvement in CER and 56.5% improvement in cpCER, compared to the offical baseline system.
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References · 8
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
- 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 574
- 2020DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancementcited by 141
- 2023CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Maskingcited by 102
- 2022Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkitcited by 97
- 2020Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenariocited by 68
- 2022High Fidelity Speech Enhancement with Band-split RNNcited by 16
- 2022GPU-accelerated Guided Source Separation for Meeting Transcriptioncited by 10
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