Sarcasm recognition in speech using a real-time approach

Date of Publication


Document Type

Master's Thesis

Degree Name

Master of Science in Computer Science

Subject Categories

Computer Sciences


College of Computer Studies


Computer Science

Thesis Adviser

Merlin Teodosia C. Suarez

Defense Panel Chair

Jocelynn Cu

Defense Panel Member

Katrina Ysabel Solomon
Macario II Cordel


This study focuses on the recognition of sarcasm in speech, and attempts to address the problem of inaccuracy with regard to identifying this particular audio signal. This problem widens the gap between computers and humans since interactions are not completely understandable using sarcastic comments. With this in mind, the goal is to create a model capable of identifying sarcasm which is generic enough to work not only on a specic set but also on any kind of sarcastic statements using audio signals. This was accomplished using machine learning and digital signal processing techniques appropriate for real time processing. Audio features like pitch, intensity, Mel Frequency Cepstral Coe cients (MFCC), and formants were experimented on using a new acted speech corpus that was annotated as sarcastic and non sarcastic by six participants which include the researcher. By using Support Vector Machine with polynomial kernel on a data set containing 0.4 second segments with 30% overlap, an accuracy and kappa of 69% and 0.39, respectively. The results suggest that pitch, intensity and certain MFCC and formant features are predictive of sarcasm. With only 10 features, SVM with polynomial kernel processes a single 0.4 second clip in 1.2 seconds making it suitable for real time processing.

Abstract Format






Accession Number


Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

1 computer optical disc ; 4 3/4 in.


Speech perception; Machine learning; Signal processing—Digital techniques

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