MFCC Feature Extraction for Speech Emotion Recognition

In speech signal processing, MFCC (Mel Frequency Cepstral Coefficients) feature extraction effectively distinguishes different emotional states. MFCC is a widely-used signal processing technique that decomposes speech signals into frame segments and extracts characteristic parameters from each frame, including energy levels, frequency components, and formant features. These parameters represent fundamental speech characteristics such as speaker identity, speech rate, pitch variations, and emotional states. The MFCC algorithm typically involves several computational steps: pre-emphasis to enhance high frequencies, framing and windowing using Hamming windows to minimize spectral leakage, Fast Fourier Transform (FFT) for frequency domain conversion, Mel-filterbank application to simulate human auditory perception, logarithmic compression for dynamic range adjustment, and finally Discrete Cosine Transform (DCT) to decorrelate the features into cepstral coefficients. Key programming considerations include frame size optimization (typically 20-40ms), overlap adjustment (commonly 10-15ms), and Mel-filterbank design with 20-40 triangular filters. The resulting MFCC vectors (usually 12-13 coefficients plus energy) serve as compact representations for machine learning applications. Consequently, MFCC technology finds extensive applications in speech recognition systems, emotion classification models, speaker verification algorithms, and other audio analytics domains where robust feature representation is crucial.