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@article{170719,
        author = {Kamran Ahmad and Gibrail Zaidi and Mr. Umashanker Sharma and Md Amber Khan and Harsh Bhati},
        title = {Voice Connect : Speech-to-Speech Translation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1610-1613},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170719},
        abstract = {As the world becomes increasingly interconnected, the ability to communicate across language barriers is essential. Speech-to-Speech Translation (SST) systems aim to facilitate real-time communication between speakers of different languages by converting spoken input in one language to spoken output in another. This paper proposes a comprehensive framework for Voice Connect, a speech-to-speech translation system, which integrates cutting-edge technologies such as Automatic Speech Recognition (ASR), Neural Machine Translation (NMT), and Text-to-Speech (TTS) synthesis. By employing deep learning techniques, the system is designed to handle multilingual speech translation with improved accuracy, naturalness, and speed. The paper outlines the methodology for developing such an end-to-end system, detailing the individual modules and their integration. Furthermore, experimental results are presented, demonstrating the system’s capability to accurately and efficiently translate speech in real-time. The paper concludes with an evaluation of the system’s performance, highlighting challenges such as latency and robustness, and suggesting future improvements to make real-time multilingual communication more accessible.},
        keywords = {},
        month = {December},
        }