Whether MFCC or GFCC is Better for Recognizing Emotion From Speech? a Study

A major challenge for Automatic Speech Recognition (ASR) relates to significant performance reduction in noisy environments. Recently, the study of the emotional content of speech signals got more importance and hence, many systems have been proposed to identify the emotional content of a spoken utterance. The important aspects of the design of a speech emotion recognition system are pre-processing, feature extraction, training and classification, recognition. Typically, extracted speaker features are short-time cepstral coefficients such as MelFrequency Cepstral Coefficients (MFCCs) and Perceptual Linear Predictive (PLP) coefficients, or long-term features such as prosody.

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