  Self-attention mechanisms have enabled transformers to achieve
superhuman-level performance on many speech-to-text (STT) tasks, yet the
challenge of automatic prosodic segmentation has remained unsolved. In this
paper we finetune Whisper, a pretrained STT model, to annotate intonation unit
(IU) boundaries by repurposing low-frequency tokens. Our approach achieves an
accuracy of 95.8%, outperforming previous methods without the need for
large-scale labeled data or enterprise grade compute resources. We also
diminish input signals by applying a series of filters, finding that low pass
filters at a 3.2 kHz level improve segmentation performance in out of sample
and out of distribution contexts. We release our model as both a transcription
tool and a baseline for further improvements in prosodic segmentation.
