Development of speech emotion recognition system using deep belief networks in malayalam language

2017 ◽  
Author(s):  
Athira Chandran ◽  
D. Pravena ◽  
D. Govind
Keyword(s):  
Emotion Recognition ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Malayalam Language

Speech emotion recognition based on Bi-directional LSTM architecture and deep belief networks

2021 ◽  
Author(s):  
N. Senthilkumar ◽  
S. Karpakam ◽  
M. Gayathri Devi ◽  
R. Balakumaresan ◽  
P. Dhilipkumar
Keyword(s):  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Belief Networks ◽  
Deep Belief Networks

scholarly journals Random Deep Belief Networks for Recognizing Emotions from Speech Signals

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Guihua Wen ◽  
Huihui Li ◽  
Jubing Huang ◽  
Danyang Li ◽  
Eryang Xun
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Majority Voting ◽  
Speech Emotion Recognition ◽  
Speech Signals ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Emotion Label ◽  
The Rich ◽  
Emotion Labels

Now the human emotions can be recognized from speech signals using machine learning methods; however, they are challenged by the lower recognition accuracies in real applications due to lack of the rich representation ability. Deep belief networks (DBN) can automatically discover the multiple levels of representations in speech signals. To make full of its advantages, this paper presents an ensemble of random deep belief networks (RDBN) method for speech emotion recognition. It firstly extracts the low level features of the input speech signal and then applies them to construct lots of random subspaces. Each random subspace is then provided for DBN to yield the higher level features as the input of the classifier to output an emotion label. All outputted emotion labels are then fused through the majority voting to decide the final emotion label for the input speech signal. The conducted experimental results on benchmark speech emotion databases show that RDBN has better accuracy than the compared methods for speech emotion recognition.

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