Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages

• URL: http://arxiv.org/abs/2409.10429v1
• Date: Mon, 16 Sep 2024 16:04:16 GMT
• Title: Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages
• Authors: Ming-Hao Hsu, Kuan Po Huang, Hung-yi Lee,
• Abstract summary: Meta-Whisper is a novel approach to improve automatic speech recognition for low-resource languages. It enhances Whisper's ability to recognize speech in unfamiliar languages without extensive fine-tuning.
• Score: 51.12146889808824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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