Augmented set of features for confidence estimation in spoken term detection

Javier Tejedor, Doroteo T. Toledano, Miguel Bautista, Simon King, Dong Wang, Jose Colas

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Discriminative confidence estimation along with confidence normalisation have been shown to construct robust decision maker modules in spoken term detection (STD) systems. Discriminative confidence estimation, making use of termdependent features, has been shown to improve the widely used lattice-based confidence estimation in STD. In this work, we augment the set of these term-dependent features and show a significant improvement in the STD performance both in terms of ATWV and DET curves in experiments conducted on a Spanish geographical corpus. This work also proposes a multiple linear regression analysis to carry out the feature selection. Next, the most informative features derived from it are used within the discriminative confidence on the STD system.
Original languageEnglish
Title of host publicationProc. Interspeech 2010
Publication statusPublished - 2010

Fingerprint Dive into the research topics of 'Augmented set of features for confidence estimation in spoken term detection'. Together they form a unique fingerprint.

Cite this