91 citations · 255 across the 16 of their papers we have counts for
6 papers · 1 filter
SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation
Xutai Ma, Juan Pino, Philipp Koehn
Simultaneous text translation and end-to-end speech translation have recently made great progress but little work has combined these tasks together. We investigate how to adapt sim…
Streaming Simultaneous Speech Translation with Augmented Memory Transformer
Xutai Ma, Yongqiang Wang, Mohammad Javad Dousti +2
Transformer-based models have achieved state-of-the-art performance on speech translation tasks. However, the model architecture is not efficient enough for streaming scenarios sin…
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks
Yun Tang, Juan Pino, Changhan Wang +2
Attention-based sequence-to-sequence modeling provides a powerful and elegant solution for applications that need to map one sequence to a different sequence. Its success heavily r…
fairseq S2T: Fast Speech-to-Text Modeling with fairseq
Changhan Wang, Yun Tang, Xutai Ma +4
We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq's car…
SimulEval: An Evaluation Toolkit for Simultaneous Translation
Xutai Ma, Mohammad Javad Dousti, Changhan Wang +2
Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario where the model starts translating before reading the complete source input. Evalua…
Self-Training for End-to-End Speech Translation
Juan Pino, Qiantong Xu, Xutai Ma +2
One of the main challenges for end-to-end speech translation is data scarcity. We leverage pseudo-labels generated from unlabeled audio by a cascade and an end-to-end speech transl…