Phonology based Fuzzy Phoneme Recognition

Abstract

Speech enhanced applications are fundamentally based on an effective separation and recognition of phonetic units. The variable nature of speech signal makes it challenging due to higher rate of variability in the phonetic units. This nature of variability is addressed with the help of Fuzzy Logic in this work for identification of phonemes. The feature parameters representing the speech signal variability in the work are Zero Crossing Rate (ZCR) and Short Term Energy (STE). The approach used in the presented work demonstrate to be more effective than other methods such as Artificial Neural Networks (ANN). A Fuzzy Logic model is tuned using features with various phonemes. Phonology based fuzzy decision logic is used to infer about phonetic units separation point. Phonetic analysis has been done for different spoken words of English language identify the vowel recognition.