SCDNet: Self-supervised Learning Feature-based Speaker Change Detection
<|reference_start|>SCDNet: Self-supervised Learning Feature-based Speaker Change Detection: Speaker Change Detection (SCD) is to identify boundaries among speakers in a conversation. Motivated by the success of fine-tuning wav2vec 2.0 models for the SCD task, a further investigation of self-supervised learning (SSL) features for SCD is conducted in this work. Specifically, an SCD model, named SCDNet, is proposed. With this model, various state-of-the-art SSL models, including Hubert, wav2vec 2.0, and WavLm are investigated. To discern the most potent layer of SSL models for SCD, a learnable weighting method is employed to analyze the effectiveness of intermediate representations. Additionally, a fine-tuning-based approach is also implemented to further compare the characteristics of SSL models in the SCD task. Furthermore, a contrastive learning method is proposed to mitigate the overfitting tendencies in the training of both the fine-tuning-based method and SCDNet. Experiments showcase the superiority of WavLm in the SCD task and also demonstrate the good design of SCDNet.<|reference_end|>
@article{li2024scdnet:,
    title={SCDNet: Self-supervised Learning Feature-based Speaker Change Detection},
    author={Yue Li, Xinsheng Wang, Li Zhang, Lei Xie},
    journal={arXiv preprint arXiv:2406.08393},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08393},
    primaryClass={eess.AS cs.SD}
}