Abstract
Emotion recognition research has gradually become an important research topic in the field of artificial intelligence. The emotion recognition services provided in the market are facial and text emotion recognition. Speech emotion recognition is rarely available. Speech is the basic method of communication between people and should contain certain information to express the emotions of the speaker. This study uses the IEMOCAP dataset to split the training set and the test set to conduct independent experiments with 10 speakers. The speech is converted to Log-Mel Spectrogram as the data pre-processing and feature extraction method. The model is based on the convolutional neural network, using Residual Shortcut, Group Convolution and Squeeze-and-Excitation to construct a deep learning model as a method of speech emotion recognition.
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