  Typical ASR systems segment the input audio into utterances using purely
acoustic information, which may not resemble the sentence-like units that are
expected by conventional machine translation (MT) systems for Spoken Language
Translation. In this work, we propose a model for correcting the acoustic
segmentation of ASR models for low-resource languages to improve performance on
downstream tasks. We propose the use of subtitles as a proxy dataset for
correcting ASR acoustic segmentation, creating synthetic acoustic utterances by
modeling common error modes. We train a neural tagging model for correcting ASR
acoustic segmentation and show that it improves downstream performance on MT
and audio-document cross-language information retrieval (CLIR).
