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Information fusion benefits delineation in off-nominal scenarios

The potential problem of deterioration in recognition system performance because of imprecise, incomplete or imperfect training is a serious challenge inherent to most-real-world applications. This problem is often referred to in certain applications as degradation of performance under off-nominal conditions. This study presents the result of an investigation carried out to illustrate the scope and benefits of information fusion in such off-nominal scenarios. The research covers features in-decision out (FEI-DEO) fusion as well as decisions in-decision out fusion (DEI-DEO). The latter spans across both information sources and multiple processing tools (classifiers). The investigation delineates the corresponding fusion benefit domains using as an example, real-world data from an audio-visual system for the recognition of French oral vowels embedded in carious levels of acoustical noise.