Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data
<|reference_start|>Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data: Recent advances in automatic speech recognition (ASR) often rely on large speech foundation models for generating high-quality transcriptions. However, these models can be impractical due to limited computing resources. The situation is even more severe in terms of more realistic or difficult scenarios, such as code-switching ASR (CS-ASR). To address this, we present a framework for developing more efficient models for CS-ASR through knowledge distillation using realistic speech-only data. Our proposed method, Leave No Knowledge Behind During Knowledge Distillation (K$^2$D), leverages both the teacher model's knowledge and additional insights from a small auxiliary model. We evaluate our approach on two in-domain and two out-domain datasets, demonstrating that K$^2$D is effective. By conducting K$^2$D on the unlabeled realistic data, we have successfully obtained a 2-time smaller model with 5-time faster generation speed while outperforming the baseline methods and the teacher model on all the testing sets. We have made our model publicly available on Hugging Face (https://huggingface.co/andybi7676/k2d-whisper.zh-en).<|reference_end|>
@article{tseng2024leave,
    title={Leave No Knowledge Behind During Knowledge Distillation: Towards
  Practical and Effective Knowledge Distillation for Code-Switching ASR Using
  Realistic Data},
    author={Liang-Hsuan Tseng, Zih-Ching Chen, Wei-Shun Chang, Cheng-Kuang Lee,
  Tsung-Ren Huang, Hung-yi Lee},
    journal={arXiv preprint arXiv:2407.10603},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10603},
    primaryClass={eess.AS cs.CL cs.SD}
}