Word recognition using hidden control neural architecture

Neural networks are used to model nonlinear and time-varying systems. The proposed model attempts to cope with the time variability systems by adding an undetermined control input which modulates the mapping implemented by the network. The network architecture proposed, the hidden control neural network (HCNN), combines nonlinear prediction of conventional neural networks with hidden Markov modeling. This network is trained using an algorithm that is based on back-propagation and segmentation algorithms for estimating the unknown control together with the network´s parameters. The HCNN approach is evaluated on multispeaker recognition of connected digits, yielding a word accuracy of 99.3%