Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages
<|reference_start|>Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages: This paper presents Meta-Whisper, a novel approach to improve automatic speech recognition (ASR) for low-resource languages using the Whisper model. By leveraging Meta In-Context Learning (Meta-ICL) and a k-Nearest Neighbors (KNN) algorithm for sample selection, Meta-Whisper enhances Whisper's ability to recognize speech in unfamiliar languages without extensive fine-tuning. Experiments on the ML-SUPERB dataset show that Meta-Whisper significantly reduces the Character Error Rate (CER) for low-resource languages compared to the original Whisper model. This method offers a promising solution for developing more adaptable multilingual ASR systems, particularly for languages with limited resources.<|reference_end|>
@article{hsu2024meta-whisper:,
    title={Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages},
    author={Ming-Hao Hsu, Kuan Po Huang, Hung-yi Lee},
    journal={arXiv preprint arXiv:2409.10429},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10429},
    primaryClass={eess.AS cs.CL cs.SD}
}