Extracting MFCC Features For Emotion Recognition From Audio Speech Signals

A major challenge for automatic speech recognition (ASR) relates to significant performance reduction in noisy environments. Recent research has shown that auditory features based on Gammatone filters are promising to improve robustness of ASR systems against noise, though the research is far from extensive and generalizability of the new features is unknown. This paper presents our implementation of the Gammatone filterbased feature along with BPNN and the experimental results on English speech data. By some thorough designs, we obtained significant performance gains with the new feature in various noise conditions when compared with the widely used MFCC. The whole simulation has been taken place in MATLAB.