Fuzzy Integrals For The Aggregation Of Confidence Measures In Speech Recognition

This paper presents a study on merging confidence measures using fuzzy logic. Instead of the previous approaches using the notion of probability, we propose to observe the uncertainty of the recognition hypotheses and the notion of possibility thanks to fuzzy reasoning. Four different confidence measures are developed, coming from different parts of a speech recognizer. Various merging methods are studied to improve the performance of the confidence measures. The methods are evaluated in terms of Confidence Error Rate (CER) and in terms of their Detection Error Tradeoff (DET) curves on a French broadcast news corpus. They are compared to some fuzzy logic aggregation techniques among which the technique based on the Choquet Integral yields to a significant improvement in terms of CER.