A Knowledge based Approach Using Fuzzy Inference Rules for Vowel Recognition

Automatic speech recognition by machine is one of the most efficient methods for man-machine communications. Because speech waveform is nonlinear and variant. Speech recognition requires a lot of intelligence and fault tolerance in the pattern recognition algorithms. Accurate vowel recognition forms the backbone of most successful speech recognition systems. A collection of techniques exists to extract the relevant features from the steady-state regions of the vowels both in time as well as in frequency domains. This paper is, introducing fuzzy techniques allow the classification of imprecise vowel data. By incorporating the acoustic attribute, the system acquires the capacity to correctly classify imprecise speech data input. Experimental results show that the fuzzy system’s performance is vastly improved over a standard Mel frequency cepstral coefficient (MFCC) features analysis of vowel recognition. The speech recognition is a particularly difficult classification problem, due to differences in voice frequency (amongst speakers) and variations in pronunciation.