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

• URL: http://arxiv.org/abs/2102.05766v1
• Date: Wed, 10 Feb 2021 22:53:40 GMT
• Title: Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation
• Authors: Renjie Zheng and Junkun Chen and Mingbo Ma and Liang Huang
• Abstract summary: 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. Experiments on three translation directions show that our proposed speech translation models fine-tuned from FAT-MLM substantially improve translation quality.
• Score: 21.622039537743607
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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