Speech emotion recognition using deep neural networks: matlab implementation
The key issues pivotal for successful Speech Emotion Recognition (SER) system are driven by a selection of proper emotional feature extraction techniques. In this book, Mel-frequency Cepstral Coefficient (MFCC) and Teager Energy Operator (TEO) along with a fusion of MFCC and TEO is examined over mul...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
LAP LAMBERT Academic Publishing
2021
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/88786/7/88786_Speech%20emotion%20recognition%20using%20deep%20neural%20networks.pdf http://irep.iium.edu.my/88786/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English |
| Summary: | The key issues pivotal for successful Speech Emotion Recognition (SER) system are driven by a selection of proper emotional feature extraction techniques. In this book, Mel-frequency Cepstral Coefficient (MFCC) and Teager Energy Operator (TEO) along with a fusion of MFCC and TEO is examined over multilingual databases consisting of English, German and Hindi languages. Deep Neural Networks (DNN) has been used for the classification of the different emotions considered, including happy, sad, angry, and neutral. A sample of Matlab code implementation is provided in this book. The proposed system could be implemented especially in the customer service application, in which TEO-based features and DNN could be used to better handle customers during a conversation. |
|---|