Abstract
In a world emphasizing multilingual interactions in a meeting, this study showcases advanced audio and text processing for accurate multilingual transcription, enhancing international collaboration, ensuring clear understanding across diverse linguistic backgrounds. Harnessing the capabilities of DPTNet, this study achieves superior sound source separation, isolating speech from ambient noise. The pyannote toolkit excels in speaker diarization, segmenting audio based on speaker identities. The SpeechRecognition module showcases its prowess in transcribing dialogue with unparalleled accuracy. Highlighting advancements in textual summarization, the research underscores the synergistic power of the TextRank algorithm and the BART model in distilling extensive narratives into succinct and abstractive summaries. The Hugging Face Transformers, especially the MarianMTModel and MarianTokenizer, provide exemplary translation from audio transcripts. Collectively, these methodologies present a comprehensive blueprint for navigating and deciphering multilingual meetings with precision and clarity.
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Mehendale, G., Kale, C., Khatri, P., Goswami, H., Shah, H., Bagul, S. (2024). Multilingual Meeting Management with NLP: Automated Minutes, Transcription, and Translation. In: Sharma, H., Shrivastava, V., Tripathi, A.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2023. Lecture Notes in Networks and Systems, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-97-2079-8_24
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DOI: https://doi.org/10.1007/978-981-97-2079-8_24
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