Time Window Analysis for Automatic Speech Emotion Recognition

In this paper we present time analysis results of speech emotion recognition using convolutional neural network architecture and spectrograms as a speech features. Analyses were performed on model with two convolutional layers followed by pooling layer, and one fully-connected layer followed by dropout and softmax layer on the output. On this model we analyzed time characteristics of speech signal represented by spectrograms. The aim of our work was to find relation between duration of speech signal and the recognition rate of seven basic emotions. It was discovered that speech length is important and naturally the accuracy is growing with the length of analyzed window, however over approximately 1.2 seconds the growth becomes rather mild.