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Low Resource ASR: The Surprising Effectiveness of High Resource Transliteration

Cross-lingual transfer of knowledge from high-resource languages to low-resource languages is an important research prob-lem in automatic speech recognition (ASR). We propose a new strategy of transfer learning by pretraining using large amounts of speech in the high-resource language but with its text transliterated to the target low-resource language. This simple map-ping of scripts explicitly encourages increased sharing between the output spaces of both languages and is surprisingly effective even when the high-resource and low-resource languages are from unrelated language families. The utility of our proposed technique is more evident in very low-resource scenarios, where better initializations are more beneﬁcial. We evaluate our technique on a transformer ASR architecture and the state-of-the-art wav2vec2.0 ASR architecture, with English as the high-resource language and six languages as low-resource targets. With access to 1 hour of target speech, we obtain relative WER reductions of up to 8.2% compared to existing transfer-learning approaches.