COMBINATION OF ACOUSTIC MODELS IN CONTINUOUS SPEECH RECOGNITION HYBRID SYSTEMS

The combination of multiple sources of information has been an attractive approach in different areas. That is the case of speech recognition area where several combination methods have been presented. Our hybrid MLP/HMM sys-tems use acoustic models based on different set of features and different MLP classier structures. In this work we de-veloped a method combining phoneme probabilities gen-erated by the different acoustic models trained on distinct feature extraction processes. Two different algorithms were implemented for combining the acoustic models probabil-ities. The rst covers the combination in the probability domain and the second one in the log-probability domain. We made combinations of two and three alternative base-line systems where was possible to obtain relative improve-ments on word error rate larger than 20 % for a large vocab-ulary speaker independent continuous speech recognition task. 1