A Study on Reducing Latency and Handling Accent Variation in Real-time Speech Translation Systems

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Real-time speech translation, or RST, sits at the heart of cross-language instant communication. It shows up in plenty of places now—cross-border video meetings, commercial translation gadgets, smart wearables, and so on. Still, two bottlenecks keep holding back practical deployment: latency control and accent robustness. Most existing reviews tend to zoom in on just one dimension, leaving the interplay between the two largely overlooked. This paper takes a systematic look at the core technologies and recent progress around latency optimization and accent adaptation in RST, covering work from 2020 to 2026. The approach draws on literature research, classification, and comparative analysis. Different technical paths get examined for what they do well and where they fall short. A recurring tension emerges—the trade-off between cutting latency and handling accent variation. The real difficulty lies in hitting a three-way balance: low latency, high accuracy, and strong accent robustness all at once. The paper also maps out shared research gaps in the field and points toward future directions worth pursuing. The aim is to offer some theoretical reference and practical guidance for the next wave of technical breakthroughs and real-world RST deployment.