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Break the Limitation of Training Data — A Better Encoder Enhanced by BERT for Speech Translation

Speech translation (ST) has increasing demand in our daily life and work. Applications like travel assistant, simultaneous conference translation and movie subtitling can highly reduce translation costs. Building a ST system that can understand and directly translate acoustic speech signals into text in a target language is challenging. For example, people do not always premeditate what they are going to say. Not like text translation, ST lacks completed organization sometimes. Another part is that the parallel corpus for ST is not enough, compared to the MT task. Especially, most ST methods are limited by the amount of parallel corpus.


Zichen ChenAbout 5 minSTDL4MTSpeech TranslationBERT
Learning Shared Semantic Space for Speech-to-Text Translation

How to develop a translation model that can take both speech and text as input and translate to target language? Can we borrow inspiration from human brain study to improve the speech translation models?

Reading Time: About 15 minutes.


Xianjun YangAbout 10 minSTDL4MTSpeech TranslationShared Semantic MemoryChimera