VOICE DRIVEN MULTILINGUAL SENTIMENT ANALYSIS SYSTEM

ABSTRACT: This paper presents a comprehensive Artificial Intelligence (AI)-driven framework for real-time speech transcription, multilingual translation, and intelligent summarization designed to enhance communication efficiency and documentation accuracy in globally distributed collaborative environments. With the rapid growth of remote work, virtual meetings, and international cooperation, there is an increasing demand for intelligent systems capable of converting spoken interactions into structured, meaningful, and accessible textual representations. The proposed platform integrates advanced speech recognition and Natural Language Processing (NLP) techniques to accurately transcribe spoken content, extract critical insights, and generate context-aware summaries across multiple languages. A deep learning-based speech-to-text model ensures robust performance across diverse accents and acoustic conditions. The system further incorporates sentiment analysis and topic modeling to capture emotional tone and thematic structure within conversations. By combining extractive and abstractive summarization techniques, the framework produces coherent and concise summaries that preserve contextual integrity. A multilingual neural machine translation module enables seamless cross-language conversion of transcripts and summaries, thereby promoting inclusive communication among linguistically diverse users. The proposed solution is applicable across corporate, academic, healthcare, and governmental domains, where it reduces manual documentation effort while improving accessibility, reliability, and decision-making efficiency. Keywords: Artificial Intelligence, Speech-to-Text, Multilingual Summarization, Natural Language Processing, Neural Machine Translation, Sentiment Analysis