Time-Frequency Feature and AMS-GMM Mask for Acoustic Emotion Classification

In this letter, the pH time-frequency vocal source feature is proposed for multistyle emotion identification. A binary acoustic mask is also used to improve the emotion classification accuracy. Emotional and stress conditions from the Berlin Database of Emotional Speech (EMO-DB) and Speech under Simulated and Actual Stress (SUSAS) databases are investigated in the experiments. In terms of emotion identification rates, the pH outperforms the mel-frequency cepstral coefficients (MFCC) and a Teager-Energy-Operator (TEO) based feature. Moreover, the acoustic mask achieves accuracy improvement for both the MFCC and the pH feature.