1 citations · 2 across the 10 of their papers we have counts for
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Do LLMs Need Architectural Changes for Simultaneous Speech Translation? A Prefix-to-Prefix Data Driven Approach
Junkun Chen, Jian Xue, Ming Tang +4
Simultaneous speech translation (SimulST) requires incremental translation under strict latency constraints, yet remains challenging for decoder-only LLM systems due to limited con…
PHRASED: Phrase Dictionary Biasing for Speech Translation
Peidong Wang, Jian Xue, Rui Zhao +3
Phrases are essential to understand the core concepts in conversations. However, due to their rare occurrence in training data, correct translation of phrases is challenging in spe…
Soft Language Identification for Language-Agnostic Many-to-One End-to-End Speech Translation
Peidong Wang, Jian Xue, Jinyu Li +2
Language-agnostic many-to-one end-to-end speech translation models can convert audio signals from different source languages into text in a target language. These models do not nee…
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning
Jing Pan, Jian Wu, Yashesh Gaur +4
We present a cost-effective method to integrate speech into a large language model (LLM), resulting in a Contextual Speech Model with Instruction-following/in-context-learning Capa…
Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation
Sara Papi, Peidong Wang, Junkun Chen +4
The growing need for instant spoken language transcription and translation is driven by increased global communication and cross-lingual interactions. This has made offering transl…
Improving Stability in Simultaneous Speech Translation: A Revision-Controllable Decoding Approach
Junkun Chen, Jian Xue, Peidong Wang +2
Simultaneous Speech-to-Text translation serves a critical role in real-time crosslingual communication. Despite the advancements in recent years, challenges remain in achieving sta…