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arXiv · 2509.01200

SimulMEGA: MoE Routers are Advanced Policy Makers for Simultaneous Speech Translation

Chenyang Le·Bing Han·Jinshun Li·Songyong Chen·Yanmin Qian
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Abstract

Simultaneous Speech Translation (SimulST) enables real-time cross-lingual communication by jointly optimizing speech recognition and machine translation under strict latency constraints. Existing systems struggle to balance translation quality, latency, and semantic coherence, particularly in multilingual many-to-many scenarios where divergent read and write policies hinder unified strategy learning. In this paper, we present SimulMEGA (Simultaneous Generation by Mixture-of-Experts Gating), an unsupervised policy learning framework that combines prefix-based training with a Mixture-of-Experts refiner to learn effective read and write decisions in an implicit manner, without adding inference-time overhead. Our design requires only minimal modifications to standard transformer architectures and generalizes across both speech-to-text and text-to-speech streaming tasks. Through comprehensive evaluation on six language pairs, our 500M parameter speech-to-text model outperforms the Seamless baseline, achieving under 7 percent BLEU degradation at 1.5 seconds average lag and under 3 percent at 3 seconds. We further demonstrate the versatility of SimulMEGA by extending it to streaming TTS with a unidirectional backbone, yielding superior latency quality tradeoffs.

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Chenyang Le, Bing Han, Jinshun Li, Songyong Chen, Yanmin Qian. 2025-10-29. SimulMEGA: MoE Routers are Advanced Policy Makers for Simultaneous Speech Translation. https://arxiv.org/abs/2509.01200

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