  In this work, we focus on improving ASR output segmentation in the context of
low-resource language speech-to-text translation. ASR output segmentation is
crucial, as ASR systems segment the input audio using purely acoustic
information and are not guaranteed to output sentence-like segments. Since most
MT systems expect sentences as input, feeding in longer unsegmented passages
can lead to sub-optimal performance. We explore the feasibility of using
datasets of subtitles from TV shows and movies to train better ASR segmentation
models. We further incorporate part-of-speech (POS) tag and dependency label
information (derived from the unsegmented ASR outputs) into our segmentation
model. We show that this noisy syntactic information can improve model
accuracy. We evaluate our models intrinsically on segmentation quality and
extrinsically on downstream MT performance, as well as downstream tasks
including cross-lingual information retrieval (CLIR) tasks and human relevance
assessments. Our model shows improved performance on downstream tasks for
Lithuanian and Bulgarian.
