The discrete emotional description model labels human emotions as discrete adjectives. The model can only represent limited types of single and explicit emotion. The dimensional emotional model quantifies the implied state of complex emotions from the multiple dimensions. In addition

conventional speech emotion feature

Mel Frequency Cepstral Coefficient (MFCC)

has the problem of neglecting the correlation between the adjacent frame spectral features due to frame division processing

making it susceptible to loss of much useful information. To solve this problem

this paper proposes an improved method

which extracts the time firing series feature and the firing position information feature from the spectrogram to supplement the MFCC

and applies them in speech emotion estimation respectively. Based on the predicted values

the proposed method calculates the correlation coefficients of each feature from three dimensions

P (Pleasure-displeasure)

A (Arousal-nonarousal)

and D (Dominance-submissiveness)

as feature weights and obtains the final values of PAD in emotion speech after the weighted fusion

and finally maps it to PAD 3D emotion space. The experiments showed that the two added features could not only detect the emotional state of the speaker

but also consider the correlation between the adjacent frame spectral features

complementing to MFCC features. On the basis of improving the effect of discrete estimation of basic emotional types

this method represents the estimation results as coordinate points in PAD 3D emotion space

adopts the quantitative method to reveal the position and connection of various emotions in the emotion space

and indicates the emotion content mixed in the emotion speech. This study lays a foundation for subsequent research on classification estimation of complex speech emotions.