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This paper presents a dual-pipeline intelligent communication framework designed to address limitations in current multilingual systems by integrating a fine-tuned large language model (LLM) with neural text-to-speech (TTS) synthesis. Leveraging a multilingual corpus exceeding 680 billion tokens, the system supports real-time translation across 24 languages, with an emphasis on Asian and South Asian linguistic families. The LLM ensures semantic, idiomatic, and grammatical accuracy, while the TTS engine, utilizing Tacotron-2 and HiFi-GAN, delivers human-like prosody. A core feature, Continuous Conversation Mode, maintains contextual memory, ensuring coherent multi-turn dialogues. Evaluations yield a BLEU score of 42.3 and a MOS of 4.2, outperforming existing tools. This technology promises significant impact in healthcare, diplomacy, education, and cross-cultural collaboration.
"Dual-Pipeline Intelligent Framework For Real-Time Contextual Multilingual Communication Using LLM And Neural TTS", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b902-b907, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504212.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator