  The purpose of speech emotion recognition system is to classify speakers
utterances into different emotional states such as disgust, boredom, sadness,
neutral and happiness. Speech features that are commonly used in speech emotion
recognition rely on global utterance level prosodic features. In our work, we
evaluate the impact of frame level feature extraction. The speech samples are
from Berlin emotional database and the features extracted from these utterances
are energy, different variant of mel frequency cepstrum coefficients, velocity
and acceleration features.
