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

Multilingual Bottleneck Features for Query by Example Spoken Term Detection