Automated Office Meeting Summarization with NLP- Based Video Transciption and Speaker Diarization

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dc.contributor.authorAkash B - 1NH20AI006
dc.contributor.authorC Sumukh - 1NH20AI019
dc.contributor.authorGowardhan Reddy V - 1NH20AI032
dc.contributor.authorV Hashith 1NH20AI111
dc.date.accessioned2024-10-21T11:26:27Z
dc.date.available2024-10-21T11:26:27Z
dc.date.issued2024
dc.description.abstractAutomatic Speech Recognition (ASR) technology has revolutionized the way to capture and transcribe spoken words, enabling effective documentation and analysis of verbal communication. Despite significant advances, existing ASR systems for office meetings face persistent problems such as limited speaker diarization, insufficient punctuation recovery, and variable accuracy across languages. These limitations often result in transcripts that are difficult to follow and less useful for detailed meeting records. This project aims to develop an improved ASR system tailored specifically for office meeting environments. By utilizing advanced deep learning models and integrating features such as speaker diarization and punctuation recovery
dc.identifier.urihttp://192.168.75.5:4000/handle/123456789/15688
dc.titleAutomated Office Meeting Summarization with NLP- Based Video Transciption and Speaker Diarization
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