Penalty functions for evaluation measures of unsegmented speech retrieval

Abstract

This paper deals with evaluation of information retrieval from unsegmented speech. We focus on Mean Generalized Average Precision, the evaluation measure widely used for unsegmented speech retrieval. This measure is designed to allow certain tolerance in matching retrieval results (starting points of relevant segments) against a gold standard relevance assessment. It employs a Penalty Function which evaluates non-exact matches in the retrieval results based on their distance from the beginnings of their nearest true relevant segments. However, the choice of the Penalty Function is usually ad-hoc and does not necessary reflect users' perception of the speech retrieval quality. We perform a lab test to study satisfaction of users of a speech retrieval system to empirically estimate the optimal shape of the Penalty Function. © 2012 Springer-Verlag.