A speech emotion recognition method and system integrating crowd information

Abstract

The invention belongs to the field of artificial intelligence, and specifically relates to a voice emotion recognition method and system that integrates crowd information. The method includes the following steps: S1, collecting user voice signals; S2, preprocessing the voice signals, and obtaining the mel spectrum; S3, removing Silent segments before and after the Mel spectrum; S4. Obtaining in-depth crowd information through the crowd classification network; S5. Obtaining the depth information of the Mel spectrum through the Mel spectrum preprocessing network; S6. Obtaining fusion information through SENet fusion features; S7. Through the classification network , to get the emotion recognition structure. The present invention fuses crowd information features to make emotional feature extraction more accurate, performs information fusion through the channel attention mechanism of SENet, can effectively extract deep features, and improves overall recognition accuracy.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a speech emotion recognition method and system for fusing crowd information. Background technique [0002] Language interaction is one of the earliest ways of human communication, so speech has become the main way for humans to express emotions. With the rise of human-computer interaction, intelligent speech sentiment analysis is becoming more and more important. At present, the main classification of emotions is the seven emotions proposed by Ekman in the last century, namely: neutral, happy, sad, angry, afraid, disgusted, and surprised. [0003] The current mainstream speech emotion recognition methods are based on traditional algorithms or deep learning methods based on simple neural network architectures. The basic process based on the traditional method is: feature extraction of speech, and emotion classification of speech through features. Among them, t...

AI Technical Summary

Problems solved by technology

[0004] In the current technology, because only the shallow information of speech is considered and a simple network structure is used, the recognition rate of emotion recognition is relatively low, and the generalization is relatively poor.

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