Abstract
A new pattern matching algorithm, employing fuzzy logic for speech recognition, is proposed. In the field of speech recognition, there are two problems to overcome before realizing a speaker-independent speech recognition machine. They are firstly, fluctuation of pattern length, and secondly, that of frequency, caused by the variance of utterance speed, and differences of resonance frequency of the vocal tract, respectively. In the described algorithm these fluctuations are translated into membership functions. The membership functions are regarded as templates for pattern matching. In this paper, after the basic theory of recognition is described, the theory is modified to a form easily used by machine. As an experimental result a recognition rate of 93.2%, enrolling 120 words, was achieved in the case of Japanese. In the case of other languages 92.8% and 93.7% were achieved for English and German, respectively. These results indicate this method is realistically accurate. It seems to be worth noting that these results were achieved using a simple algorithm and accordingly the machine is small and inexpensive.
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