PHONETICALLY-BASED MULTILAYERED NEURAL NETWORKS FOR VOWEL CLASSIFICATION

The vowel sub-component of a speaker-independent phoneme classification system will be described. The architecture of the vowel classifier is based on an ear model followed by a set of Multi-Layered Neural Networks (MLNN). MLNNs are trained to learn how to recognize articulatory features like the place of articulation and the manner of articulation related to tongue position. Experiments are performed on 10 English vowels showing a recognition rate higher than 95% on new speakers. When features are used for recognition, comparable results are obtained for vowels and diphthongs not used for training and pronounced by new speakers. This suggests that MLNNs suitably fed by the data computed by an ear model have good generalization capabilities over new speakers and new sounds. © 1990.