An i-vector GPLDA system for speech based emotion recognition

In this paper, we propose the use of a Gaussian Probabilistic Linear Discriminant Analysis (GPLDA) back-end for utterance level emotion classification based on i-vectors representing the distribution of frame level MFCC features. Experimental results based on the IEMOCAP corpus show that the GPLDA back-end outperforms an SVM based back-end while being less sensitive to i-vector dimensionality, making the proposed framework more robust to parameter tuning during system development.