Topics on Minimum Classification Error Rate Based Discriminant Function Approach to Speech Recognition

In this paper, we study discriminant function based minimum recognition error rate pattern recognition approach. This approach departs from the conventional paradigm which links a classification/recognition task to the problem of distribution estimation. Instead, it takes a discriminant function based statistical pattern recognition approach and the goodness of this approach to classification error rate minimization is established through a special loss function. It is meaningful even when the model correctness assumption is known not valid. The use of discriminant function has a significant impact on classifier design, since in many realistic applications, such as speech recognition, the true distribution form of the source is rarely known precisely and without model correctness assumption, the classical optimality theory of the distribution estimation approach can not be applied directly. We discuss issues in this new classifier design paradigm and present various extensions of this approach for applications in speech processing.