Abstract
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.
Highlights
The composition of simple but nonlinear modules results in deep-learning model
An Autoencoder is constructed in this paper by stacking two layers; First layer is used to classify the category of speech that is normal or autistic and the second layer is used to classify the emotion of that category that is Angry, Happy, Neutral and Sad
To improve the Chinese speech emotion recognition, a novel speech emotion recognition algorithm based on stack autoencoder, denoise autoencoder and sparse autoencoder is proposed [15]
Summary
The composition of simple but nonlinear modules results in deep-learning model. An Autoencoder is constructed in this paper by stacking two layers; First layer is used to classify the category of speech that is normal or autistic and the second layer is used to classify the emotion of that category that is Angry, Happy, Neutral and Sad. Auto-encoder is a stack of building block. Auto-encoder is a stack of building block It contains multiple layers of representation [12]. Comprehensive review was presented in [14] about popular deep learning algorithms for speech emotion recognition. The experimental results revealed that the proposed algorithm with stack autoencoder performs 14.3% higher than SVM.
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