Voice-activated word processor with automatic learning for dynamic optimization of syllable-templates

In this speaker-dependent recognition system, recognition is based on syllable template matching and each syllable has several templates. In the initial training for each speaker, 590 templates for 111 syllables are made, each including various contextual variations. The authors studied a learning method in which the syllable templates are automatically optimized. It is judged whether or not an input syllable should be learned according to the recent recognition condition. If it should be learned, the input syllable pattern replaces the template that contributes the least to recognition in the templates segmented from the same context and in the same syllable category. Automatic learning was evaluated on recognition of speech data obtained by reading Japanese sentences at a rate of about 4 to 5 syllables per second. The results over eight speakers showed an average syllable recognition accuracy of 71.0% without and 82.5% with automatic learning. Further, by increasing the maximum number of templates to 1024, it rose to 84.8%.