  State of the art solutions to query by example spoken term detection
(QbE-STD) usually rely on bottleneck feature representation of the query and
audio document to perform dynamic time warping (DTW) based template matching.
Here, we present a study on QbE-STD performance using several monolingual as
well as multilingual bottleneck features extracted from feed forward networks.
Then, we propose to employ residual networks (ResNet) to estimate the
bottleneck features and show significant improvements over the corresponding
feed forward network based features. The neural networks are trained on
GlobalPhone corpus and QbE-STD experiments are performed on a very challenging
QUESST 2014 database.
