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.