DiaCorrect: Error Correction Back-end For Speaker Diarization

• URL: http://arxiv.org/abs/2309.08377v1
• Date: Fri, 15 Sep 2023 13:08:12 GMT
• Title: DiaCorrect: Error Correction Back-end For Speaker Diarization
• Authors: Jiangyu Han, Federico Landini, Johan Rohdin, Mireia Diez, Lukas Burget, Yuhang Cao, Heng Lu, Jan Cernocky
• Abstract summary: We propose an error correction framework, named DiaCorrect, to refine the output of a diarization system. Our model consists of two parallel convolutional encoders and a transform-based decoder.
• Score: 9.311650017389262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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