Non-autoregressive Transformer (NAT) significantly accelerates the inference of neural
machine translation. However, conventional NAT models suffer from limited expression …

Simultaneous machine translation (SiMT) models are trained to strike a balance between
latency and translation quality. However, training these models to achieve high quality while …

Recently proposed two-pass direct speech-to-speech translation (S2ST) models decompose
the task into speech-to-text translation (S2TT) and text-to-speech (TTS) within an end-to-end …

Direct speech-to-speech translation (S2ST) has achieved impressive translation quality, but
it often faces the challenge of slow decoding due to the considerable length of speech …

Non-autoregressive Transformers (NATs) are recently applied in direct speech-to-speech
translation systems, which convert speech across different languages without intermediate …

Automatic Speech-to-Speech Translation (S2ST) is crucial for VR, providing immersive
experiences and global accessibility. For this task, cascade pipelines are often used, but it …