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.

本文细调了一个预训练的 STT 模型 Whisper，将低频令牌重新用于标注语调单元（IU）边界，精度达到 95.8％，优于以往的方法，无需大规模标记数据或企业级计算资源，并且通过应用一系列滤波器来减小输入信号，发现在 3.2 kHz 级别下的低通滤波器可以提高样本外和分布外语境下的分段性能，我们将该模型作为转录工具并作为进一步改进语调分割的基准线释放。