AI-Driven Speech-to-Speech Translation with Real-Time Lip Synchronization for Educational Applications

Educational videos rely heavily on clear speech and natural visual cues to support meaningful learning. However, standard text-based or audio-only translation methods lack lip movements, which often results in reduced comprehension and a less engaging experience for learners. We introduce an end-toend speech-to-speech translation framework that provides realtime lip synchronization for multilingual educational content. Our system combines silence-aware audio segmentation, modular translation and speech-generation pipelines, and Wav2Lipbased visual alignment to generate lip-synchronized videos in five target languages: English, Hindi, Bengali, Telugu, and Nepali. It also incorporates a fallback mechanism that handles frames without detectable faces, thereby preserving visual continuity even in challenging video segments. The entire pipeline is deployed as a fully functional web platform and is suitable for real-world classroom and remote-learning scenarios. We evaluate the framework through a multi-language user study with native speakers of all supported languages. The results show strong inter-annotator agreement for Lip-Sync Quality (Fleiss'$\kappa=0.8872$) and Translation Quality ($\kappa{=}$0.8992), demonstrating that the generated videos are both accurate and natural to watch. Participants consistently preferred the lip-synchronized outputs over audio-only translations, citing improvements in clarity and visual coherence. Overall, our findings highlight the framework's potential to make educational content more inclusive and accessible to a diverse range of learners worldwide.