  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.
