Abstract
The presence of overlapping speech has a significant negative impact on the performance of speaker diarization systems. In this paper, we employ a convolutional neural network for the detection of such speech intervals and evaluate it in terms of the potential improvements to speaker diarization. We train the network on specifically-created synthetic data, while the evaluation is performed on the AMI Corpus and the SSPNet Conflict Corpus.
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CITATION STYLE
Kunešová, M., Hrúz, M., Zajíc, Z., & Radová, V. (2019). Detection of overlapping speech for the purposes of speaker diarization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11658 LNAI, pp. 247–257). Springer Verlag. https://doi.org/10.1007/978-3-030-26061-3_26
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