  Prosodic boundaries in speech are of great relevance to both speech synthesis
and audio annotation. In this paper, we apply the wav2vec 2.0 framework to the
task of detecting these boundaries in speech signal, using only acoustic
information. We test the approach on a set of recordings of Czech broadcast
news, labeled by phonetic experts, and compare it to an existing text-based
predictor, which uses the transcripts of the same data. Despite using a
relatively small amount of labeled data, the wav2vec2 model achieves an
accuracy of 94% and F1 measure of 83% on within-sentence prosodic boundaries
(or 95% and 89% on all prosodic boundaries), outperforming the text-based
approach. However, by combining the outputs of the two different models we can
improve the results even further.
