Voice Based Sarcasm Detection in Kannada Language


  • Manohar R. Research Scholar, Department of Computer Science and Engineering, Sir M. Visvesvaraya Institute of Technology, Visvesvaraya Technological University, Belagavi, India
  • Suma Swamy Professor, Department of Computer Science and Engineering, Sir M. Visvesvaraya Institute of Technology, Visvesvaraya Technological University, Belagavi, India


Sarcasm Detection, Kannada Language Processing, Non-uniform Fast Fourier Transform, Audio, Voice Recognition, Speech Analysis


In recent times usage of social media has increased exponentially. Sarcasm has become a common way of expressing their discontent. Sarcasm is often used to express their dissatisfaction by taunting others. It is commonly expressed by varying the tone and slang of the language. Most of the existing work on sarcasm has been focused on textual data and very little work has been carried out on audio and video data. Audio data gives us as a lot of information when compare to textual data for categorising whether the given statement is sarcastic or non -sarcastic. Very little work is done on sarcasm detection in Indian languages especially on Kannada language. Textual data may not always give us the correct message without considering the circumstances or the sentiment around. In order to find out the amount of sarcasm in the statement we have to take in to consideration to the sentiment behind the statement as well. In this regard it becomes very important to not the expression of the speaker.  The tone of the speaker and the accent matter a lot considering the language being used. The dialect and the repetitive words slang and tone matter a lot. This paper focuses on using audio data to identify sarcasm in Kannada language using deep learning approach.


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How to Cite

R., M. ., & Swamy, S. . (2024). Voice Based Sarcasm Detection in Kannada Language. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 356–367. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4672



Research Article