Aug 2026· Next-Generation Computing Systems and Technologies· Vol 2, pp. 70-75· 0 citations· 11 references
Abstract
Taking notes during meetings sounds easy, but in reality, people often miss key points—especially when multiple people are talking. Managing follow-ups in separate apps only adds to the hassle. MinuteMaster was built to simplify this. It's a web app that brings transcription, speaker identification, summarization, and scheduling into one place. It uses Whisper for multilingual speech-to-text, pyannote.audio to identify who's speaking, and BART to turn long transcripts into clear, short summaries. A built-in calendar helps users manage meetings without switching apps. We tested it on 30 recordings across English, Hindi, Telugu, Tamil, and mixed languages. Transcription accuracy ranged from 76\% to 88\%, and summaries performed better than TextRank with a ROUGE-1 score of 0.48. In a study with 42 users, it received an average rating of 4.25/5. The system runs on Flask with MongoDB, making it simple to deploy and scale.
Manually preparing minutes of a meeting is time-consuming and error-prone, especially in the multilingual case, such as that in India, where the same meeting can use Tamil, Telugu, Hindi, Kannada and English languages. In this paper we propose a pipeline to automate the preparation of meeting minutes by utilizing multi...
P. G. Krishna, T. Thilagam· International Workshop on Ar...· 0 citations
India's 22 scheduled languages are spoken by over a billion people in conversations that mix languages, often within one turn, overlap freely, and take place over telephones, in meetings, and in media. Knowing who spoke when, the task of speaker diarization, is essential to speech applications for such conversation: vo...
Asked to describe what one of six speakers in a recording talks about, audio language models describe the right one on 6 to 16% of trials, below the 16.7% a guess would give, and a second finding concerns how such heads are found.
Conversation Coach is proposed, a voice-first AI system that enables managers to rehearse difficult workplace conversations in a realistic spoken format and offers superior reasoning essential for coaching quality.
Wu Fanyou, Suraj Maharjan, Ainur Yessenalina et al.· 0 citations
A conversational speech model has to decide, at every pause, whether to speak. Existing evaluations test boundary detection, policy adherence, or aggregate statistics, not this decision. We place real-time speech systems at 427 pauses and 482 turn ends from 176 unscripted conversations in Seamless Interaction, give eac...
TurnBench, a multi-domain benchmark that pairs a 30-hour, hand-labeled corpus of dyadic human conversation with a standardized evaluation protocol for end-of-turn and interruption detection, finds end-of-turn recall stable across types, while interruption false positives are strongly type-dependent and concentrated in...
Freeman Jiang, Ramon Sanabria, Soham Deshmukh et al.· 9 citations· ⚡4
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