Performance Evaluation of Deep Autoencoder Network for Speech Emotion Recognition

The learning methods with multiple levels of representation is called deep learning methods. The composition of simple but now linear modules results in deep-learning model. Deep-learning in near future will have many more success, because it requires very little engineering in hands and it can easi...

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Veröffentlicht in:International journal of advanced computer science & applications Jg. 11; H. 2
Hauptverfasser: AndleebSiddiqui, Maria, Hussain, Wajahat, Abbas, Syed, -, Danish-ur-Rehman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: West Yorkshire Science and Information (SAI) Organization Limited 2020
Schlagworte:
Accuracy
Autistic children
Classification
Coders
Deep learning
Emotion recognition
Emotions
Machine learning
Performance evaluation
Speech recognition
Teaching methods
ISSN:2158-107X, 2156-5570
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Zusammenfassung:The learning methods with multiple levels of representation is called deep learning methods. The composition of simple but now linear modules results in deep-learning model. Deep-learning in near future will have many more success, because it requires very little engineering in hands and it can easily take ample amount of data for computation. In this paper the deep learning network is used to recognize speech emotions. The deep Autoencoder is constructed to learn the speech emotions (Angry, Happy, Neutral, and Sad) of Normal and Autistic Children. Experimental results evident that the categorical classification accuracy of speech is 46.5% and 33.3% for Normal and Autistic children speech respectively. Whereas, Auto encoder shows a very low classification accuracy of 26.1% for only happy emotion and no classification accuracy for Angry, Neutral and Sad emotions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110276