In this work, we propose an error correction framework, named DiaCorrect, to refine the output of a diarization system in a simple yet effective way. This method is inspired by error correction techniques in automatic speech recognition. Our model consists of two parallel convolutional encoders and a transform-based decoder. By exploiting the interactions between the input recording and the initial system's outputs, DiaCorrect can automatically correct the initial speaker activities to minimize the diarization errors. Experiments on 2-speaker telephony data show that the proposed DiaCorrect can effectively improve the initial model's results. Our source code is publicly available at https://github.com/BUTSpeechFIT/diacorrect.
@article{arxiv.2309.08377,
title = {DiaCorrect: Error Correction Back-end For Speaker Diarization},
author = {Jiangyu Han and Federico Landini and Johan Rohdin and Mireia Diez and Lukas Burget and Yuhang Cao and Heng Lu and Jan Cernocky},
journal= {arXiv preprint arXiv:2309.08377},
year = {2023}
}