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Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation

2021-02-10 22:53:40
Renjie Zheng, Junkun Chen, Mingbo Ma, Liang Huang
arXiv_CL
         
Abstract

Recently text and speech representation learning has successfully improved many language related tasks. However, all existing methods only learn from one input modality, while a unified acoustic and text representation is desired by many speech-related tasks such as speech translation. We propose a Fused Acoustic and Text Masked Language Model (FAT-MLM) which jointly learns a unified representation for both acoustic and text in-put. Within this cross modal representation learning framework, we further present an end-to-end model for Fused Acoustic and Text Speech Translation (FAT-ST). Experiments on three translation directions show that our proposed speech translation models fine-tuned from FAT-MLM substantially improve translation quality (+5.90 BLEU).

Abstract (translated)

URL

https://arxiv.org/abs/2102.05766

PDF

https://arxiv.org/pdf/2102.05766.pdf

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