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Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages by Hsu, Ming-Hao; Huang, Kuan Po; Lee, Hung-yi is a scholarly article available to read on EtoBox.

What is Meta-Whisper: Speech-Based Meta-ICL for ASR on Low-Resource Languages about?

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.

Author

Hsu, Ming-Hao; Huang, Kuan Po; Lee, Hung-yi

Published

2024

Language

EN