  Spoken Term Detection (STD) is the task of searching for words or phrases
within audio, given either text or spoken input as a query. In this work, we
use state-of-the-art Hindi, Tamil and Telugu ASR systems cross-lingually for
lexical Spoken Term Detection in ten low-resource Indian languages. Since no
publicly available dataset exists for Spoken Term Detection in these languages,
we create a new dataset using a publicly available TTS dataset. We report a
standard metric for STD, Mean Term Weighted Value (MTWV) and show that ASR
systems built in languages that are phonetically similar to the target
languages have higher accuracy, however, it is also possible to get high MTWV
scores for dissimilar languages by using a relaxed phone matching algorithm. We
propose a technique to bootstrap the Grapheme-to-Phoneme (g2p) mapping between
all the languages under consideration using publicly available resources. Gains
are obtained when we combine the output of multiple ASR systems and when we use
language-specific Language Models. We show that it is possible to perform STD
cross-lingually in a zero-shot manner without the need for any
language-specific speech data. We plan to make the STD dataset available for
other researchers interested in cross-lingual STD.
