  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.
