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This paper describes a syllable-proximity evaluation task within the context of an automatic speech recognition application. This task is well suited to a multiple-information aggregation framework in which preliminary evaluations of separate information sources are combined to produce a more accurate and reliable overall evaluation than would otherwise be the case. An aggregation operator using fuzzy measures and a fuzzy integral is adopted that possesses a number of desirable properties and the fuzzy-measure parameters can be automatically learned from training data by re-casting the syllable-proximity evaluation as a classification problem. Experiments performed on spontaneous speech material demonstrate that the fuzzy-integration-based aggregation approach has many advantages over alternative techniques in terms of both performance and interpretability of the system.