Real-Time Speech-to-Speech Translator: Analysis and Implementation

Speech-to-speech translation (S2ST) technology bridges language barriers, enabling seamless communication across cultures. This paper presents a modular system integrating Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) for real-time translation. Leveraging Python, Google APIs, and self-supervised models, the system achieves high accuracy (ASR: 90– 95%, MT: 85–90%) and low latency (2–3 seconds). Key contributions include noise filtering, scalable architecture, and support for low-resource languages. Applications span healthcare, education, and global collaboration, emphasizing the practical significance of this innovation.