Effect of fuzzification on the plosive cognition system

Abstract

Fuzzy algorithms provide a simpler and more powerful approach than statistical decision methods for describing non-ideal (fuzzy) environments in which there exists no precise boundary between the categories due to inherent vagueness rather than randomness. This paper attempts to demonstrate the effectiveness of such an algorithm when applied to the computer recognition of patterns of biological origin such as Telugu unaspirated plosives in initial position of large number of utterances in CVC context. A multieategorizer is described in which the fuzzy processor embodies a fuzzy property extractor and a similarity matrix generator. A provision fur controlling fuzziness in property sets had been made by keeping two parameters. 'exponential' and 'denominational' fuzzifiers, in the components of property matrices; their effect on recognition score is also studied. Machines' performances are explained by plotting curves and through confusion matrices when transition, duration and slope of transition from the point of transient release of stop closure to the steady state of only first two formants were used as input features. Voiced stops are differentiated more easily than unvoiced stops, with the maximum overall recognition score ranging from 60% for dentals to 85% for bilabials. The fuzzy hedge 'slightly' when applied to property sets reduces the confusion from that of the hedge 'very' and consecutive utilizations of the operations 'CONT', 'OIL' and 'INT' resulted in a wide variation of about 20 to 25% in the recognition score. Such a variation is found to be insignificant beyond an optimum value of the exponential fuzzifier'.