  Speech segmentation is an essential part of speech translation (ST) systems
in real-world scenarios. Since most ST models are designed to process speech
segments, long-form audio must be partitioned into shorter segments before
translation. Recently, data-driven approaches for the speech segmentation task
have been developed. Although the approaches improve overall translation
quality, a performance gap exists due to a mismatch between the models and ST
systems. In addition, the prior works require large self-supervised speech
models, which consume significant computational resources. In this work, we
propose a segmentation model that achieves better speech translation quality
with a small model size. We propose an ASR-with-punctuation task as an
effective pre-training strategy for the segmentation model. We also show that
proper integration of the speech segmentation model into the underlying ST
system is critical to improve overall translation quality at inference time.
