  Current approaches to speech emotion recognition focus on speech features
that can capture the emotional content of a speech signal. Mel Frequency
Cepstral Coefficients (MFCCs) are one of the most commonly used representations
for audio speech recognition and classification. This paper proposes Gammatone
Frequency Cepstral Coefficients (GFCCs) as a potentially better representation
of speech signals for emotion recognition. The effectiveness of MFCC and GFCC
representations are compared and evaluated over emotion and intensity
classification tasks with fully connected and recurrent neural network
architectures. The results provide evidence that GFCCs outperform MFCCs in
speech emotion recognition.
