Recent Advances in Direct Speech-to-text Translation

• URL: http://arxiv.org/abs/2306.11646v1
• Date: Tue, 20 Jun 2023 16:14:27 GMT
• Title: Recent Advances in Direct Speech-to-text Translation
• Authors: Chen Xu, Rong Ye, Qianqian Dong, Chengqi Zhao, Tom Ko, Mingxuan Wang, Tong Xiao, Jingbo Zhu
• Abstract summary: We categorize the existing research work into three directions based on the main challenges -- modeling burden, data scarcity, and application issues. For the challenge of data scarcity, recent work resorts to many sophisticated techniques, such as data augmentation, pre-training, knowledge distillation, and multilingual modeling. We analyze and summarize the application issues, which include real-time, segmentation, named entity, gender bias, and code-switching.
• Score: 58.692782919570845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly. In this paper, we present a comprehensive survey on direct speech translation aiming to summarize the current state-of-the-art techniques. First, we categorize the existing research work into three directions based on the main challenges -- modeling burden, data scarcity, and application issues. To tackle the problem of modeling burden, two main structures have been proposed, encoder-decoder framework (Transformer and the variants) and multitask frameworks. For the challenge of data scarcity, recent work resorts to many sophisticated techniques, such as data augmentation, pre-training, knowledge distillation, and multilingual modeling. We analyze and summarize the application issues, which include real-time, segmentation, named entity, gender bias, and code-switching. Finally, we discuss some promising directions for future work.

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