  Self-supervised learning approaches have lately achieved great success on a
broad spectrum of machine learning problems. In the field of speech processing,
one of the most successful recent self-supervised models is wav2vec 2.0. In
this paper, we explore the effectiveness of this model on three basic speech
classification tasks: speaker change detection, overlapped speech detection,
and voice activity detection. First, we concentrate on only one task -- speaker
change detection -- where our proposed system surpasses the previously reported
results on four different corpora, and achieves comparable performance even
when trained on out-of-domain data from an artificially designed dataset. Then
we expand our approach to tackle all three tasks in a single multitask system
with state-of-the-art performance on the AMI corpus. The implementation of the
algorithms in this paper is publicly available at
https://github.com/mkunes/w2v2_audioFrameClassification.
