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arXiv · 2309.16247

PP-MeT: a Real-world Personalized Prompt based Meeting Transcription System

Xiang Lyu·Yuhang Cao·Qing Wang·Jingjing Yin·Yuguang Yang·Pengpeng Zou·Yanni Hu·Heng Lu
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Abstract

Speaker-attributed automatic speech recognition (SA-ASR) improves the accuracy and applicability of multi-speaker ASR systems in real-world scenarios by assigning speaker labels to transcribed texts. However, SA-ASR poses unique challenges due to factors such as speaker overlap, speaker variability, background noise, and reverberation. In this study, we propose PP-MeT system, a real-world personalized prompt based meeting transcription system, which consists of a clustering system, target-speaker voice activity detection (TS-VAD), and TS-ASR. Specifically, we utilize target-speaker embedding as a prompt in TS-VAD and TS-ASR modules in our proposed system. In constrast with previous system, we fully leverage pre-trained models for system initialization, thereby bestowing our approach with heightened generalizability and precision. Experiments on M2MeT2.0 Challenge dataset show that our system achieves a cp-CER of 11.27% on the test set, ranking first in both fixed and open training conditions.

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Xiang Lyu, Yuhang Cao, Qing Wang, Jingjing Yin, Yuguang Yang, Pengpeng Zou, Yanni Hu, Heng Lu. 2023-09-28. PP-MeT: a Real-world Personalized Prompt based Meeting Transcription System. https://arxiv.org/abs/2309.16247

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