Image Pattern Classification Using MFCC and HMM

We propose a novel method for recognizing temporally or spatially varying patterns using MFCC (mel-frequency ceptral coefficient) and HMM (hidden Markov model). MFCC and HMM have been adopted as de facto standard for speech recognition. It is very useful in modeling time-domain signals with temporally varying characteristics. Most images have characteristical patterns, so HMM is expected to model them very efficiently. We suggest efficient pattern classification algorithm with MFCC and HMM, and showed its improved performance in MNISTand fashion MNIST databases.