LARGE VOCABULARY SPEAKER-INDEPENDENT CONTINUOUS SPEECH RECOGNITION WITH A NEW HYBRID SYSTEM BASED ON MMI-NEURAL NETWORKS

This paper presents a new hybrid system for speaker independent continuous speech recognition in a large vocabulary task. The hybrid system is a combination of context dependent discrete Hidden Markov Models and artificial neural networks that are trained by an information theory based algorithm. This algorithm maximizes the Mutual /nformation (MMI) between the network output and the phone descriptions by applying a self-organizing learning approach instead of forcing constrained network outputs. Recognition results have shown that the new hybrid system outperforms a classical k-means-VQ-based HMM-system. For the speaker independent DARPA Resource Management (RM) task (perplexity 60) we report a decrease in word recognition error rate up to 35% (close to the best continuous pdf systems).