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arXiv · 2509.21932

SimulSense: Sense-Driven Interpreting for Efficient Simultaneous Speech Translation

Haotian Tan·Hiroki Ouchi·Sakriani Sakti
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Abstract

How to make human-interpreter-like read/write decisions for simultaneous speech translation (SimulST) systems? Current state-of-the-art systems formulate SimulST as a multi-turn dialogue task, requiring specialized interleaved training data and relying on computationally expensive large language model (LLM) inference for decision-making. In this paper, we propose SimulSense, a novel framework for SimulST that mimics human interpreters by continuously reading input speech and triggering write decisions to produce translation when a new sense unit is perceived. Experiments against two state-of-the-art baseline systems demonstrate that our proposed method achieves a superior quality-latency tradeoff and substantially improved real-time efficiency, where its decision-making is up to 9.6x faster than the baselines.

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Haotian Tan, Hiroki Ouchi, Sakriani Sakti. 2025-09-26. SimulSense: Sense-Driven Interpreting for Efficient Simultaneous Speech Translation. https://arxiv.org/abs/2509.21932

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