Class definition in discriminant feature analysis

The aim of discriminant feature analysis techniques in the signal processing of speech recognition systems is to find a feature vector transformation which maps a high dimensional input vector onto a low dimensional vector while retaining a maximum amount of information in the feature vector to discriminate between predefined classes. This paper points out the significance of the definition of the classes in the discriminant feature analysis technique. Three choices for the definition of the classes are investigated: the phonemes, the states in context independent acoustic models and the tied states in context dependent acoustic models. These choices for the classes were applied to (1) standard LDA (linear discriminant analysis) for reference and to (2) MIDA, an improved, mutual information based discriminant analysis technique. Evaluation of the resulting linear feature transforms on a large vocabulary continuous speech recognition task shows, depending on the technique, the best choice for the classes.