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Robust voiced/unvoiced speech classification using fuzzy rules

The paper presents a robust voiced/unvoiced speech classifier based on fuzzy logic. More specifically, the classification is based on a pattern recognition approach in which the matching phase is performed using a set of 5 fuzzy rules obtained by training. Certain interesting statistical properties of the fuzzy system allow the transition threshold to be adapted to the level of background noise. The results show that the performance of the fuzzy classifier in the presence of various types of background noise is better than that of traditional methods.