A Study on Speech Emotion Recognition Using a Deep Neural Network
| Autor: | Byung Tae Jang, Do Hyun Kim, Kyong Hee Lee, Hyun Kyun Choi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Artificial neural network business.industry Computer science Speech recognition Deep learning 020208 electrical & electronic engineering 02 engineering and technology 01 natural sciences Support vector machine 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Emotion recognition Artificial intelligence business |
| Zdroj: | ICTC |
| Popis: | When using voice signals as input to a deep learning network, there may be myriad features depending on the method and purpose of extracting the voice signal features. Therefore, extraction of appropriate features should be conducted. In this study, verbal features necessary for speech emotion recognition (SER) and preprocessing features for a deep neural network are described in detail. We implemented various preprocessing methods using voice features. Also, a Keras-based deep neural network using Python libraries was implemented. With these features, we could obtain a test accuracy of 68.5 % using the deep neural network (DNN). As a result, we confirmed that the proposed DNN improved an accuracy by 30.1 % compared to a support vector machine (SVM). |
| Databáze: | OpenAIRE |
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