Research Article
Research on the Construction of Human-Computer Interaction System Based on a Machine Learning Algorithm
Table 1
Spectral characterization.
| Extract characteristics | Advantage | Shortcoming |
| MFCC | Popular features. | Poor noise resistance. | LPCC | Helps to capture the voice perception of the human ear. | For different emotions (especially anger and sadness), the coefficient values usually overlap. | ZCR | Delta and double-delta values can improve recognition accuracy. | The ZCR value tends to vary greatly, depending on the amount of noise present. | Shimmer | Indicates common features of voice content. | Emotions such as anger and disgust often exhibit similar jitters and flickers. | LFPC | The observed LFPC value is not relevant, so the diagonal covariance of its value can be used as the feature input of the classifier. | Most studies only compare LFPC with MFCC and LPCC. The nonlinear changes of the speech signal are not considered. | DSCC | Simple calculation. | Anger and disgust often exhibit similar jitters and flickers. |
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