Autoassociator-based modular architecture for speaker independent phoneme recognition

Proposes a modular architecture where the interactions among different modules are controlled by proper autoassociators. The outputs of these modules are computed by sigma p-neurons whose inputs come from both a feedforward network performing classification and an autoassociator. The outputs of the autoassociators are used for performing pattern rejection, thus reducing significantly the problems due to interaction of different modules. The proposed architecture is validated by experiments of speaker independent phoneme recognition on continuous speech with TIMIT data base with very promising results