• Premlata Sevakram Patil
• Dr. Harsh Lohiya

This article aims to develop a comprehensive system capable of facilitating speech translation across various languages using a three-stage model. The system's core components include Speech Recognition, Machine Translation, and Speech Synthesis, each utilizing distinct tools and methodologies. Speech-to-text and text-to-speech conversions are managed using Google APIs, while the translation process is powered by a Recurrent Neural Network (RNN) model. The study delves into the RNN's role in enhancing translation accuracy and provides a detailed explanation of the speech synthesis component. Additionally, the system's architecture is outlined, highlighting the services and communication protocols essential for linking clients to the primary speech-to-speech translation servers. The research focuses on the intricate pipeline engineering behind automated speech recognition, machine translation, and speech synthesis, emphasizing the reliance on lexical data while addressing the challenges of incorporating rich, contextual speech information such as noise and human expressions.

Speech, Translation, Recurrent Neural Network (RNN), Machine Translation, System Architecture.

"EVALUATING THE PERFORMANCE OF RNN-BASED MODELS IN ACHIEVING ACCURATE MACHINE TRANSLATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.g885-g894, February-2024, Available :http://www.jetir.org/papers/JETIR2402710.pdf

"EVALUATING THE PERFORMANCE OF RNN-BASED MODELS IN ACHIEVING ACCURATE MACHINE TRANSLATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppg885-g894, February-2024, Available at : http://www.jetir.org/papers/JETIR2402710.pdf