  Recognizing emotion from speech has become one the active research themes in
speech processing and in applications based on human-computer interaction. This
paper conducts an experimental study on recognizing emotions from human speech.
The emotions considered for the experiments include neutral, anger, joy and
sadness. The distinuishability of emotional features in speech were studied
first followed by emotion classification performed on a custom dataset. The
classification was performed for different classifiers. One of the main feature
attribute considered in the prepared dataset was the peak-to-peak distance
obtained from the graphical representation of the speech signals. After
performing the classification tests on a dataset formed from 30 different
subjects, it was found that for getting better accuracy, one should consider
the data collected from one person rather than considering the data from a
group of people.
