Real-Time Speech Translation for Wearable Devices: A Multi-Modal Approach Using Edge Computing and Neural Machine Translation

This paper presents a conceptual framework for a real-time speech translation system optimized for resourceconstrained wearable devices, including smartwatches, wireless earbuds, and augmented reality glasses. The proposed system integrates automatic speech recognition (ASR), neural machine translation (NMT), and text-to-speech (TTS) synthesis within a hybrid edge-cloud architecture to enable low-latency, high-quality translation. The design leverages TensorFlow Lite for ondevice inference, optimized transformer architectures with model compression, and adaptive audio processing to accommodate variable acoustic conditions. Simulated evaluations indicate that the framework has the potential to achieve end-to-end translation latencies of approximately 2–3 seconds and maintain translation quality comparable to established NMT benchmarks across multiple language pairs. The architecture also supports scalable integration of multimodal data sources and can be extended to applications in mobile contexts requiring ubiquitous cross-language communication. This study provides a foundation for future experimental validation and real-world deployment of intelligent wearable translation systems.