With the continuous development of information technology and deep learning, more and more methodological ideas have penetrated into the application field of speech emotion recognition, and how to recognize the emotion expressed in human speech more scientifically and effectively has become an important issue in speech emotion recognition and neural network research. In order to solve the shortcomings of the existing research on speech emotion recognition by fusing deep confidence networks, this paper briefly introduces the sample data and parameter settings for the application of speech emotion recognition model by fusing deep confidence networks, based on the functional equations of Boltzmann machine and the training steps of deep confidence networks and the types of speech emotion recognition. The experimental data show that the recognition accuracy of deep confidence networks is higher than that of (SVN) and (RNN) models, and the average recognition accuracy of deep confidence networks reaches 96%, while the average recognition accuracy of (SVN) and (RNN) models reaches 91%, respectively. The average accuracy of (SVN) and (RNN) recognition reached 91% and 92%, respectively, thus verifying the feasibility of fusing deep confidence networks for speech emotion recognition.

Sahil Verma. Speech Emotion Recognition with Deep Belief Network. International Journal of Neural Network (2020), Vol. 1, Issue 2: 40-47. https://doi.org/10.38007/NN.2020.010206.

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