An acoustical pattern classifier based on N-depth projection on privileged eigenstructures

A geometrical vector classifier is applied to the problem of phonetic classification in several experimental environments. The algorithm is based on the measure of similarity between the original vector and the ones reconstructed using a N-depth projection on the eigenvectors related to the covariance matrix of each category to be classified. For each category (i.e. for each phoneme) there is a privileged subspace of arbitrary dimension and with N axes where the similarity of the training vector set is maximized. These geometrical subspaces are characterized in relation to databases, speaker dependence, speech emission, and signal parametrization. Experiments were performed using three small databases: a four-speaker continuous speech, a single-speaker isolated words, and a single-speaker continuous speech database. Results are reported for closed tests (where training and classification were performed on the same database), and for open tests (where they were performed on different databases). It is concluded that the proposed method may, in some cases, successfully substitute for vector quantizer techniques