Aggression in Hindi and English Speech: Acoustic Correlates and Automatic Identification

TL;DR

This paper analyzes acoustic features of aggressive political speech in Hindi and English, developing automatic classifiers that identify aggression with over 66% accuracy based solely on acoustic cues.

Contribution

It introduces new acoustic analysis of aggressive speech in Hindi and English and develops automatic classifiers trained on large annotated datasets for aggression detection.

Findings

Hindi classifier accuracy over 73%

English classifier accuracy over 66%

Identification based solely on acoustic features

Abstract

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.