Umut AvciAvci, UmutA. Karpov , R. Potapova2025-10-0620209789819698936, 9789819698042, 9789819698110, 9789819698905, 9789819512324, 9783032026019, 9783032008909, 9783031915802, 9789819698141, 9783031984136978303060275816113349, 030297430302-974310.1007/978-3-030-60276-5_62-s2.0-85092908947https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092908947&doi=10.1007%2F978-3-030-60276-5_6&partnerID=40&md5=5c1762012e819e76abe64307d68034cehttps://gcris.yasar.edu.tr/handle/123456789/9288https://doi.org/10.1007/978-3-030-60276-5_6In this paper we tackle the problem of identifying emotions from speech by using features derived from spectrogram patterns. Towards this goal we create a spectrogram for each speech signal. Produced spectrograms are divided into non-overlapping partitions based on different frequency ranges. After performing a discretization operation on each partition we mine partition-specific patterns that discriminate an emotion from all other emotions. A classifier is then trained with features obtained from the extracted patterns. Our experimental evaluations indicate that the spectrogram-based patterns outperform the standard set of acoustic features. It is also shown that the results can further be improved with the increasing number of spectrogram partitions. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessEmotion Recognition, Feature Extraction, Spectrogram, Spectrographs, Speech, Acoustic Features, Different Frequency, Discretizations, Experimental Evaluation, Spectrograms, Speech Emotion Recognition, Speech Signals, Speech RecognitionSpectrographs, Speech, Acoustic features, Different frequency, Discretizations, Experimental evaluation, Spectrograms, Speech emotion recognition, Speech signals, Speech recognitionSpectrogramEmotion RecognitionFeature ExtractionConference Object