Optimizing Dysarthria Wake-Up Word Spotting: An End-to-End Approach for SLT 2024 LRDWWS Challenge
<|reference_start|>Optimizing Dysarthria Wake-Up Word Spotting: An End-to-End Approach for SLT 2024 LRDWWS Challenge: Speech has emerged as a widely embraced user interface across diverse applications. However, for individuals with dysarthria, the inherent variability in their speech poses significant challenges. This paper presents an end-to-end Pretrain-based Dual-filter Dysarthria Wake-up word Spotting (PD-DWS) system for the SLT 2024 Low-Resource Dysarthria Wake-Up Word Spotting Challenge. Specifically, our system improves performance from two key perspectives: audio modeling and dual-filter strategy. For audio modeling, we propose an innovative 2branch-d2v2 model based on the pre-trained data2vec2 (d2v2), which can simultaneously model automatic speech recognition (ASR) and wake-up word spotting (WWS) tasks through a unified multi-task finetuning paradigm. Additionally, a dual-filter strategy is introduced to reduce the false accept rate (FAR) while maintaining the same false reject rate (FRR). Experimental results demonstrate that our PD-DWS system achieves an FAR of 0.00321 and an FRR of 0.005, with a total score of 0.00821 on the test-B eval set, securing first place in the challenge.<|reference_end|>
@article{liu2024optimizing,
    title={Optimizing Dysarthria Wake-Up Word Spotting: An End-to-End Approach for
  SLT 2024 LRDWWS Challenge},
    author={Shuiyun Liu, Yuxiang Kong, Pengcheng Guo, Weiji Zhuang, Peng Gao,
  Yujun Wang, Lei Xie},
    journal={arXiv preprint arXiv:2409.10076},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10076},
    primaryClass={cs.SD cs.HC eess.AS}
}