  This paper is devoted to improve automatic emotion recognition from speech by
incorporating rhythm and temporal features. Research on automatic emotion
recognition so far has mostly been based on applying features like MFCCs, pitch
and energy or intensity. The idea focuses on borrowing rhythm features from
linguistic and phonetic analysis and applying them to the speech signal on the
basis of acoustic knowledge only. In addition to this we exploit a set of
temporal and loudness features. A segmentation unit is employed in starting to
separate the voiced/unvoiced and silence parts and features are explored on
different segments. Thereafter different classifiers are used for
classification. After selecting the top features using an IGR filter we are
able to achieve a recognition rate of 80.60 % on the Berlin Emotion Database
for the speaker dependent framework.
