Paper ID: 2309.08377

DiaCorrect: Error Correction Back-end For Speaker Diarization

Jiangyu Han, Federico Landini, Johan Rohdin, Mireia Diez, Lukas Burget, Yuhang Cao, Heng Lu, Jan Cernocky

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.

Submitted: Sep 15, 2023