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Time-Frequency Feature and AMS-GMM Mask for Acoustic Emotion Classification | IEEE Journals & Magazine | IEEE Xplore

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

Publisher: IEEE

Abstract:

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 c...View more

Abstract:

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.
Published in: IEEE Signal Processing Letters ( Volume: 21, Issue: 5, May 2014)
Page(s): 620 - 624
Date of Publication: 12 March 2014

ISSN Information:

Publisher: IEEE

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