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 back:ground 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.