  In the present paper, we will present the results of an acoustic analysis of
political discourse in Hindi and discuss some of the conventionalised acoustic
features of aggressive speech regularly employed by the speakers of Hindi and
English. The study is based on a corpus of slightly over 10 hours of political
discourse and includes debates on news channel and political speeches. Using
this study, we develop two automatic classification systems for identifying
aggression in English and Hindi speech, based solely on an acoustic model. The
Hindi classifier, trained using 50 hours of annotated speech, and English
classifier, trained using 40 hours of annotated speech, achieve a respectable
accuracy of over 73% and 66% respectively. In this paper, we discuss the
development of this annotated dataset, the experiments for developing the
classifier and discuss the errors that it makes.
