Google Research


SLT 2022 (2022) (to appear)


Multilingual end-to-end automatic speech recognition models are attractive due to its simplicity in training and deployment. Recent work on large-scale training of such models has shown promising results compared to monolingual models. However, the work often focuses on the structure of multilingual models themselves in a single-pass decoding setup. In this work, we investigate second-pass deliberation for multilingual speech recognition. Our proposed deliberation is multilingual, i.e., the text encoder encodes hypothesis text from multiple languages, and the deliberation decoder attends to encoded text and audio from multiple languages without explicitly using language information. We investigate scaling different components of the multilingual deliberation model, such as the text encoder and deliberation decoder, and also compare scaling the second-pass deliberation decoder and the first-pass cascaded encoder. We show that deliberation improves the average WER on 9 languages by 4% relative compared to the single-pass model in a truly multilingual setup. By increasing the size of the deliberation model up to 1B parameters, the average WER improvement increases to 9%, with up to 14% for certain languages.

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