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Implementation of an Automatic Meeting Minute Generation System Using YAMNet with Speaker Identification and Keyword Prompts.
- Source :
- Applied Sciences (2076-3417); Jul2024, Vol. 14 Issue 13, p5718, 17p
- Publication Year :
- 2024
-
Abstract
- Featured Application: The proposed system can automatically generate conference/meeting minutes with labeled speakers and produce keyword spotting. So, the proposed system reduces the heavy task of recording the meeting minutes and improves concentration during meetings. Producing conference/meeting minutes requires a person to simultaneously identify a speaker and the speaking content during the course of the meeting. This recording process is a heavy task. Reducing the workload for meeting minutes is an essential task for most people. In addition, providing conference/meeting highlights in real time is helpful to the meeting process. In this study, we aim to implement an automatic meeting minutes generation system (AMMGS) for recording conference/meeting minutes. A speech recognizer transforms speech signals to obtain the conference/meeting text. Accordingly, the proposed AMMGS can reduce the effort in recording the minutes. All meeting members can concentrate on the meeting; taking minutes is unnecessary. The AMMGS includes speaker identification for Mandarin Chinese speakers, keyword spotting, and speech recognition. Transferring learning on YAMNet lets the network identify specified speakers. So, the proposed AMMGS can automatically generate conference/meeting minutes with labeled speakers. Furthermore, the AMMGS applies the Jieba segmentation tool for keyword spotting. The system detects the frequency of words' occurrence. Keywords are determined from the highly segmented words. These keywords help an attendant to stay with the agenda. The experimental results reveal that the proposed AMMGS can accurately identify speakers and recognize speech. Accordingly, the AMMGS can generate conference/meeting minutes while the keywords are spotted effectively. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20763417
- Volume :
- 14
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- Applied Sciences (2076-3417)
- Publication Type :
- Academic Journal
- Accession number :
- 178414043
- Full Text :
- https://doi.org/10.3390/app14135718