Current research on spoken language translation (SLT) has to confront with the scarcity of
sizeable and publicly available training corpora. This problem hinders the adoption of neural …

End-to-end spoken language translation (SLT) has recently gained popularity thanks to the
advancement of sequence to sequence learning in its two parent tasks: automatic speech …

In light of the recent breakthroughs in automatic machine translation systems, we propose a
novel approach that we term as" Face-to-Face Translation". As today's digital communication …

Multimodal machine translation involves drawing information from more than one modality,
based on the assumption that the additional modalities will contain useful alternative views …

Recent works in spoken language translation (SLT) have attempted to build end-to-end
speech-to-text translation without using source language transcription during learning or …

Speech-to-text translation pertains to the task of converting speech signals in a language to
text in another language. It finds its application in various domains, such as hands-free …

Neural transducers have been widely used in automatic speech recognition (ASR). In this
paper, we introduce it to streaming end-to-end speech translation (ST), which aims to …

The CMU Wilderness Multilingual Speech Dataset (Black, 2019) is a newly published
multilingual speech dataset based on recorded readings of the New Testament. It provides …

This paper presents BSTC (Baidu Speech Translation Corpus), a large-scale Chinese-
English speech translation dataset. This dataset is constructed based on a collection of …

The emergence of large language models (LLMs) has sparked significant interest in
extending their remarkable language capabilities to speech. However, modality alignment …