Speech Emotion Recognition Using Spectrogram Patterns as Features

• Published in: Speech and Computer Vol. 12335; pp. 57 - 67
• Main Author: Avci, Umut
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Emotion recognition; Feature extraction; Spectrogram
• ISBN: 3030602753; 9783030602758
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-60276-5_6

In 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.