  Besides spoken words, speech signals also carry information about speaker
gender, age, and emotional state which can be used in a variety of speech
analysis applications. In this paper, a divide and conquer strategy for
ensemble classification has been proposed to recognize emotions in speech.
Intrinsic hierarchy in emotions has been utilized to construct an emotions
tree, which assisted in breaking down the emotion recognition task into smaller
sub tasks. The proposed framework generates predictions in three phases.
Firstly, emotions are detected in the input speech signal by classifying it as
neutral or emotional. If the speech is classified as emotional, then in the
second phase, it is further classified into positive and negative classes.
Finally, individual positive or negative emotions are identified based on the
outcomes of the previous stages. Several experiments have been performed on a
widely used benchmark dataset. The proposed method was able to achieve improved
recognition rates as compared to several other approaches.
