Cross-Lingual Transferability of Self-Supervised Speech Representations for Low-Resource ASR on LibriSpeech and CommonVoice

Abstract

Abstract: This paper addresses the challenge of integrating low-resource languages into multilingual automatic speech recognition (ASR) systems. We introduce a novel application of weighted cross-entropy, typically used for unbalanced datasets, to facilitate the integration of low-resource languages into pre-trained multilingual ASR models within the context of continual multilingual learning. We fine-tune the Whisper multilingual ASR model on five high-resource languages and one low-resource language, employing language-weighted dynamic cross-entropy and data augmentation. The results show a remarkable

Research Question

How does the cross-lingual transferability of self-supervised speech representations affect downstream automatic speech recognition (ASR) performance in low-resource languages compared to high-resource languages, as measured by word error rate (WER) reductions on LibriSpeech and CommonVoice benchmarks?

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