Disambiguating Conflicting Classification Results in AVSR
Gonzalo D. Sad; Lucas D. Terissi; Juan C. Gómez Laboratory for System Dynamics and Signal Processing, Universidad Nacional de Rosario, CIFASIS-CONICET, Rosario, Argentina
Abstract
A novel scheme for disambiguating conflicting classification results in audio-visual speech recognition (AVSR) applications is proposed in this chapter. The strategy can be implemented with generative and discriminative models. It can be employed with different kinds of input information, viz., audio, visual, or audio-visual information, indistinctly. The proposed training procedure, introduces the concept of complementary models. A complementary model to a particular class j refers to a model ...
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