Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model
<|reference_start|>Integrating Self-supervised Speech Model with Pseudo Word-level Targets from Visually-grounded Speech Model: Recent advances in self-supervised speech models have shown significant improvement in many downstream tasks. However, these models predominantly centered on frame-level training objectives, which can fall short in spoken language understanding tasks that require semantic comprehension. Existing works often rely on additional speech-text data as intermediate targets, which is costly in the real-world setting. To address this challenge, we propose Pseudo-Word HuBERT (PW-HuBERT), a framework that integrates pseudo word-level targets into the training process, where the targets are derived from a visually-ground speech model, notably eliminating the need for speech-text paired data. Our experimental results on four spoken language understanding (SLU) benchmarks suggest the superiority of our model in capturing semantic information.<|reference_end|>
@article{fang2024integrating,
    title={Integrating Self-supervised Speech Model with Pseudo Word-level Targets
  from Visually-grounded Speech Model},
    author={Hung-Chieh Fang, Nai-Xuan Ye, Yi-Jen Shih, Puyuan Peng, Hsuan-Fu Wang,
  Layne Berry, Hung-yi Lee, David Harwath},
    journal={arXiv preprint arXiv:2402.05819},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05819},
    primaryClass={eess.AS cs.CL cs.LG}
}