A Method for Speech Emotion Recognition Using Emotion Perceptual Spectrum Features

Abstract

The invention relates to a method for speech emotion recognition using emotion perception spectrum features. Firstly, a pre-emphasis method is used to perform high-frequency enhancement on the input speech signal, and then the fast Fourier transform is used to convert it to a frequency to obtain a speech frequency signal; and then for the speech frequency The signal is divided into multiple sub-bands by using the emotion-aware sub-band division method; the emotional-aware spectral features are calculated for each sub-band, and the spectral features include emotional entropy features, emotional spectrum harmonic inclinations, and emotional spectrum harmonic flatness; The spectral features are calculated by global statistical features to obtain the global emotion perception spectrum feature vector; finally, the emotion perception spectrum feature vector is input to the SVM classifier to obtain the emotion category of the speech signal. According to the principle of the speech psychoacoustic model, the invention adopts the perceptual sub-band division method to accurately describe the emotional state information, and performs emotional recognition through the sub-band spectral features, and improves the recognition rate by 10.4% compared with the traditional MFCC features.

Description

technical field [0001] The invention relates to the technical field of speech emotion recognition, in particular to a speech emotion recognition method of emotion perception spectrum features. Background technique [0002] Speech is the most important way for people to communicate. Speech signals not only contain rich semantic information, but also carry rich emotional states. Analyzing the emotional features in speech and using machine learning methods to identify the emotional state of speech can be applied in many scenarios, such as: in virtual reality, by recognizing human emotions to improve the naturalness of human-computer interaction; in car driving, Improve driving safety by identifying the driver's mental state; in medicine, provide diagnostic evidence by identifying the patient's mental state; in automatic customer service, improve customer service quality by identifying customer emotions. In recent years, with the rapid development of artificial intelligence and...

AI Technical Summary

Problems solved by technology

However, emotional information is more delicate than voice content, and traditional spectral features such as MFCC and LPC are difficult to express closer emotional states, such as: sadness, fear

Images