Application of Fuzzy-integration-based Multiple-information Aggregation in Automatic Speech Recognition

Many real-world problems can be cast into a multiple-information aggregation framework where preliminary evaluations of separate information sources are combined to produce more accurate and reliable evaluation than would otherwise be the case. In this paper we describe a syllable-proximity evaluation problem in automatic speech recognition that fits well into this aggregation framework. A fuzzy-integration-based approach is adopted as the aggregation operator and a gradient-based algorithm is described for learning parameters automatically from training data. Experiments using spontaneous speech material demonstrate that the fuzzy-integration-based aggregation approach has many advantages over other techniques in terms of both performance and interpretability of the system.