SPEAKER-INDEPENDENT CONTINUOUS SPEECH RECOGNITION USING FUZZY PARTITION MODEL (FPM) AND LR PARSERS

This paper discusses speaker-independent continuous speech recognition using the neural network with a fuzzy partition model (FPM) for phoneme discrimination. Higher-speed learning can be realized with FPM than with the time-delay neural network (TDNN). Using this high-speed property allows speaker-independent phoneme discrimination learning to be realized (heretofore, long learning time was a serious drawback). This paper presents speaker-independent continuous speech recognition using the FPM-LR speech recognition system, wherein FPM is used for the phoneme discrimination and is to be combined with the LR parser. In this experiment, phoneme discrimination training is executed using as speech samples 8 males and 8 females; the recognition performance is evaluated using 278 phrases. The following observations are derived as a result of experiment. A FPM requires less training time than TDNN. The performance can be improved by using the Multi-FPM-LR system, where processes for male, female and mixed case are combined. It is useful to add the power and the delta-spectrum to the acoustic feature parameters. It is effective to use speeches of various utterances (word and phrase) in the training. An 80.0 percent recognition rate is achieved in the recognition of 278 phrases. Finally, the result for the sentence speech recognition is presented.