A Speech Emotion Recognition Method in Pad 3D Emotion Space
A technology of speech emotion recognition and emotion space, applied in speech analysis, instruments, etc., can solve the problems that the signal cannot reflect non-stationarity, large misrecognition rate, etc.
Inactive Publication Date: 2021-08-03
WUHAN UNIV OF TECH
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MFCC is the Hz spectrum feature calculated by using the nonlinear correspondence between Mel frequency and Hz frequency. The Mel frequency scale can strengthen the low-frequency details of the speech signal, so MFCC can highlight the useful information of the speech signal and reduce the impact of environmental noise on the speech signal. Interference can effectively identify speech emotion, but because the non-stationarity of emotional speech signals is particularly obvious, the direct FFT of the signal cannot reflect its non-stationarity, so using MFCC alone for speech emotion recognition has a large misrecognition rate
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[0014] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.
[0015] The CASIA Chinese emotional corpus used in this embodiment is a discrete speech emotional database developed by the Chinese Academy of Sciences Automation, but due to a completed research project, the Institute of Psychology, Chinese Academy of Sciences hired 346 college students to use its revised simplified version of the Sinicization The PAD emotion scale evaluates the PAD value of 14 specific emotional categories, and obtains the values of these 14 emotions in P (pleasure), A (activation) and D (dominance), which includes CASIA There are 6 types ...
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The invention discloses a speech emotion recognition method in a PAD three-dimensional emotion space. The PAD three-dimensional emotion model based on the dimension theory is selected as the expression mode of the recognition result, and the Mel frequency cepstral coefficient, the time ignition sequence and the ignition position information characteristics are respectively separated. For PAD value prediction of voice emotion, correlation analysis is performed from three dimensions of P (pleasure), A (activation), and D (dominance), and the weight coefficients of these three features are calculated, and the voice emotion is obtained by weighted fusion Final predicted values in PAD 3D affective space. Experiments show that this method can more carefully locate the emotional state of speech in the emotional space, pay more attention to the expression and embodiment of the inner components of emotion, and more appropriately reflect the polarity and degree of emotional expression, thus showing the mixed emotions in emotional speech content.
Description
technical field [0001] The invention belongs to the field of speech emotion recognition, and relates to a speech emotion recognition method, in particular to a speech emotion recognition method in a PAD three-dimensional emotion space. Background technique [0002] In the field of speech emotion recognition, commonly used cepstral features generally include Mel-Frequency Cepstral Coefficient (MFCC) and the like. MFCC is the Hz spectrum feature calculated by using the nonlinear correspondence between Mel frequency and Hz frequency. The Mel frequency scale can strengthen the low-frequency details of the speech signal, so MFCC can highlight the useful information of the speech signal and reduce the impact of environmental noise on the speech signal. Interference can effectively identify speech emotion, but because the non-stationarity of emotional speech signals is particularly obvious, the direct FFT of the signal cannot reflect its non-stationarity, so using MFCC alone for sp...
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IPC IPC(8): G10L25/03G10L25/24G10L25/27G10L25/63
CPCG10L25/03G10L25/24G10L25/27G10L25/63
Inventor 程艳芬陈逸灵李超
Owner WUHAN UNIV OF TECH