LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related Tasks
<|reference_start|>LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related Tasks: Self-supervised learning (SSL)-based speech models are extensively used for full-stack speech processing. However, it has been observed that improving SSL-based speech representations using unlabeled speech for content-related tasks is challenging and computationally expensive. Recent attempts have been made to address this issue with cost-effective self-supervised fine-tuning (SSFT) approaches. Continuing in this direction, a cost-effective SSFT method named "LASER: Learning by Aligning Self-supervised Representations" is presented. LASER is based on the soft-DTW alignment loss with temporal regularisation term. Experiments are conducted with HuBERT and WavLM models and evaluated on the SUPERB benchmark for two content-related tasks: automatic speech recognition (ASR) and phoneme recognition (PR). A relative improvement of 3.7% and 8.2% for HuBERT, and 4.1% and 11.7% for WavLM are observed, for the ASR and PR tasks respectively, with only < 3 hours of fine-tuning on a single GPU.<|reference_end|>
@article{meghanani2024laser:,
    title={LASER: Learning by Aligning Self-supervised Representations of Speech
  for Improving Content-related Tasks},
    author={Amit Meghanani and Thomas Hain},
    journal={arXiv preprint arXiv:2406.09153},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09153},
    primaryClass={cs.CL cs.SD eess.AS}
}