Speech Emotion Recognition Using Spectrogram Patterns as Features
| Autor: | Umut Avci |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Discretization
Computer science business.industry Feature extraction 020206 networking & telecommunications Pattern recognition 02 engineering and technology 0202 electrical engineering electronic engineering information engineering Spectrogram 020201 artificial intelligence & image processing Emotion recognition Artificial intelligence business Classifier (UML) |
| Zdroj: | Speech and Computer ISBN: 9783030602758 SPECOM |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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