Feature selection through orthogonal expansion in isolated word recognition

The use of an orthogonal expansion for feature selection and data compression in isolated word recognition is presented. Assuming that the spectral evolution, given by a LPC analysis, is a noisy measure in the sense that it is a linear combination of a set of real features, the objective of the orthogonal expansion is to find these real features. The new feature vectors are used to perform the recognition process. The recognition results as well as the meaning of the orthogonal expansion are discussed