  This paper proposes an algorithm to improve the calculation of confidence
measure for spoken term detection (STD). Given an input query term, the
algorithm first calculates a measurement named document ranking weight for each
document in the speech database to reflect its relevance with the query term by
summing all the confidence measures of the hypothesized term occurrences in
this document. The confidence measure of each term occurrence is then
re-estimated through linear interpolation with the calculated document ranking
weight to improve its reliability by integrating document-level information.
Experiments are conducted on three standard STD tasks for Tamil, Vietnamese and
English respectively. The experimental results all demonstrate that the
proposed algorithm achieves consistent improvements over the state-of-the-art
method for confidence measure calculation. Furthermore, this algorithm is still
effective even if a high accuracy speech recognizer is not available, which
makes it applicable for the languages with limited speech resources.
