Deep Learning Models for Speech Emotion Recognition
| Autor: | V M Praseetha, Sangil Vadivel |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer Networks and Communications Computer science business.industry Emotion classification Deep learning Speech recognition Feed forward 02 engineering and technology 01 natural sciences Recurrent neural network Artificial Intelligence Robustness (computer science) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Emotion recognition Artificial intelligence business 010301 acoustics Software |
| Zdroj: | Journal of Computer Science. 14:1577-1587 |
| ISSN: | 1549-3636 |
| DOI: | 10.3844/jcssp.2018.1577.1587 |
| Popis: | Emotions play a vital role in the efficient and natural human computer interaction. Recognizing human emotions from their speech is truly a challenging task when accuracy, robustness and latency are considered. With the recent advancements in deep learning now it is possible to get better accuracy, robustness and low latency for solving complex functions. In our experiment we have developed two deep learning models for emotion recognition from speech. We compare the performance of a feed forward Deep Neural Network (DNN) with the recently developed Recurrent Neural Network (RNN) which is known as Gated Recurrent Unit (GRU) for speech emotion recognition. GRUs are currently not explored for classifying emotions from speech. The DNN model gives an accuracy of 89.96% and the GRU model gives an accuracy of 95.82%. Our experiments show that GRU model performs very well on emotion classification compared to the DNN model. |
| Databáze: | OpenAIRE |
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