• Prajwal
• Sandesh Kumar
• Sanjaykumar B M

Real-time language translation is a transformative application on a Natural language processing (NLP) with machine learning that enables instantaneous communication across linguistic barriers. This technology processes spoken or written input in one language and provides immediate output in another, facilitating seamless interactions in globalized environments such as business, education, travel, and emergency services. Leveraging advancements of a speech recognition, neural machine translation, and speech synthesis, modern real-time translation systems deliver increasingly accurate and context-aware results. This paper explores the architecture, challenges, and innovations of real-time translation systems, including latency reduction, handling idiomatic expressions, and ensuring cultural sensitivity. The implementation of such systems on mobile and embedded devices opens new frontiers in accessibility and multilingual communication. Despite their advantages, real-time translators still face challenges such as handling regional accents, idiomatic expressions, and context-based meanings. However, continuous advancements in AI and machine learning are significantly improving the performance and reliability of these systems. As technology evolves, real-time language translators are expected to become more accurate, efficient, and integrated into daily life, ultimately break down of language barriers and fostering the global communication.

Real-Time Translation, NLP, Speech Recognition, Transformer, Deep Learning

"REAL-TIME LANGUAGE TRANSLATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.294-299, July-2025, Available :http://www.jetir.org/papers/JETIRGX06054.pdf

"REAL-TIME LANGUAGE TRANSLATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp294-299, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06054.pdf