SimulSense: Sense-Driven Interpreting for Efficient Simultaneous Speech Translation

2025-09-26

AI Summary

To address the high latency and computational cost in simultaneous speech-to-text translation (SimulST) caused by dialogue-based modeling and large language model (LLM) inference for read/write decisions, this paper proposes a perception-driven streaming decision mechanism—mimicking human interpreters’ real-time identification of semantic units and triggering of translation within continuous speech streams. Departing from dialogue-style training, our method integrates lightweight speech perception and semantic boundary detection modules into an end-to-end trainable, low-overhead decision architecture. Evaluated on multiple SimulST benchmarks, it achieves superior quality-latency trade-offs (ALTO), reducing average latency by 42% and accelerating decision speed by 9.6× over SOTA methods, without sacrificing translation quality. The core contribution is the first integration of cognitively inspired, perception-driven decision-making into SimulST—enabling efficient, robust, and human-intuitive streaming translation.

Technology Category

• Natural Language Processing: Speech
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Mimics human interpreters' read/write decisions
• Eliminates need for specialized interleaved training data
• Reduces computational cost of simultaneous translation decisions

Innovation

• Sense-driven framework for simultaneous speech translation
• Continuous reading with sense unit triggered decisions
• Superior quality-latency tradeoff with faster decision-making