The paper addresses the issue of confidence measure reliability provided by automatic speech recognition systems for use in various spoken language processing applications. In this context, a conditional random field (CRF)-based combination of contextual features is proposed to improve word-level confidence measures. More precisely, the method consists in combining phonetic, lexical, linguistic and semantic features to enhance confidence measures, explicitly exploiting context information. The combination is performed using CRFs whose selected patterns enable to establish a precise diagnosis about the interest of individual and contextual features. Experiments, conducted on the French broadcast news corpus ESTER, demonstrate the added-value of the proposed CRF-based combination of contextual features, with significant improvement of the normalized cross entropy and of the equal error rate.
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