Speech emotion recognition method and system based on ternary loss

Abstract

The invention belongs to the technical field of emotion recognition, particularly relates to a speech emotion recognition method and system based on ternary loss, and aims at solving the technical problem of accurately recognizing confusable emotion categories. For this purpose, the speech emotion recognition method comprises the following steps: framing the speech data to be measured so as to obtain a speech sequence of the specific length; temporal coding is performed based on the preset emotional temporal coding network and according to the speech sequence so as to obtain the emotional feature vectors corresponding to the speech sequence; and predicting the emotion category corresponding to the emotion feature vectors based on the preset speech emotion classifier and according to multiple preset real emotion categories. According to the speech emotion recognition method, the confusable speech emotion categories can be greatly recognized, and the method can be executed and implemented by the speech emotion recognition system.

Description

technical field [0001] The invention belongs to the technical field of emotion recognition, and in particular relates to a speech emotion recognition method and system based on ternary loss. Background technique [0002] Speech emotion recognition has a wide range of applications in human-computer interaction and artificial intelligence, and is a key research direction in the field of human-computer interaction and artificial intelligence. Speech emotion recognition mainly includes two parts, speech emotion feature extraction and speech emotion recognition model training. Most speech emotion recognition methods focus on extracting robust and effective speech emotion features and finding effective emotion recognition models. However, emotions are characterized by ambiguity, and some emotions are particularly easy to be confused with each other, such as the two categories of "angry" and "disgusted", and the two categories of "surprise" and "sad". [0003] In addition, there ...

AI Technical Summary

Problems solved by technology

[0003] In addition, there is a thorny problem in speech emotion recognition, which is the problem of variable input length. Traditional machine learning methods require fixed-length input information. The general method is to truncate longer samples and fill shorter samples with 0. However, The experimental results of these practices are not ideal

Images