  Recent work on unsupervised term discovery (UTD) aims to identify and cluster
repeated word-like units from audio alone. These systems are promising for some
very low-resource languages where transcribed audio is unavailable, or where no
written form of the language exists. However, in some cases it may still be
feasible (e.g., through crowdsourcing) to obtain (possibly noisy) text
translations of the audio. If so, this information could be used as a source of
side information to improve UTD. Here, we present a simple method for rescoring
the output of a UTD system using text translations, and test it on a corpus of
Spanish audio with English translations. We show that it greatly improves the
average precision of the results over a wide range of system configurations and
data preprocessing methods.
