Avcı, UmutAkkurt, GamzeUnay, Devrim2023-06-162023-06-162019978-3-030-26060-6978-3-030-26061-30302-97431611-3349https://doi.org/10.1007/978-3-030-26061-3_6https://hdl.handle.net/20.500.14365/81221st International Conference on Speech and Computer (SPECOM) -- AUG 20-25, 2019 -- Istanbul, TURKEYWe address the problem of recognizing emotions from speech using features derived from emotional patterns. Because much work in the field focuses on using low-level acoustic features, we explicitly study whether high-level features are useful for classifying emotions. For this purpose, we convert a continuous speech signal to a discretized signal and extract discriminative patterns that are capable of distinguishing distinct emotions from each other. Extracted patterns are then used to create a feature set to be fed into a classifier. Experimental results show that patterns alone are good predictors of emotions. When used to build a classifier, pattern features achieve accuracy gains up to 25% compared to state-of-the-art acoustic features.eninfo:eu-repo/semantics/closedAccessEmotion recognitionSpeech processingPattern miningFeature extractionConference Object10.1007/978-3-030-26061-3_62-s2.0-85071450258