Information theory-based supervised learning methods for self-organizing maps in combination with hidden Markov modeling

The author presents various aspects of the combination of neural networks (NNs) and hidden Markov modeling (HMM) techniques. The combination of HMM with Kohonen´s self-organizing maps is investigated in order to improve the performance of HMM-based speech recognition systems. The investigation has led to the development of a supervised learning method for the self-organizing map. This supervised learning method is based on information theory principles, leading to new design rules for the self-organizing map, making the map more suitable for combination with HMM techniques. The author also presents an information-theory-based approach for the automatic control of the map parameters during learning and a general consideration of the use of information theory principles for the design of neural networks in combination with HMM for improved processing of time-varying patterns