Umut AvciGamze AkkurtDevrim UnayAA SalahA KarpovR Potapova2025-10-062019978-3-030-26060-6, 978-3-030-26061-30302-974310.1007/978-3-030-26061-3_6http://dx.doi.org/10.1007/978-3-030-26061-3_6https://gcris.yasar.edu.tr/handle/123456789/6547We 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.EnglishEmotion recognition, Speech processing, Pattern mining, Feature extractionConference Object