AI RESEARCH
MCAT: Scaling Many-to-Many Speech-to-Text Translation with MLLMs to 70 Languages
arXiv CS.CL
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ArXi:2512.01512v2 Announce Type: replace Multimodal Large Language Models (MLLMs) have achieved great success in Speech-to-Text Translation (S2TT) tasks. However, current research is constrained by two key challenges: language coverage and efficiency. Most of the popular S2TT datasets are substantially English-centric, which restricts the scaling-up of MLLMs' many-to-many translation capabilities. Moreover, the inference speed of MLLMs degrades dramatically when the speech is converted into long sequences (e.g., 750 tokens