On Enhancing Speech Emotion Recognition using Generative Adversarial Networks
Table 2: Classification results of using synthetically generated vectors in the test set
['Dataset', 'UAR (%)']
In this experiment, in addition to using the real dataset to train the GANs, we also use them to train a SVM for emotion recognition. The synthetic samples were used in the test set. The objective of this experiment is to assess the similarity between real and synthetic data by using a model trained on real data to classify synthetic data. In case of vanilla GAN, the generated 2D representations were used as test set while the compressed 2D representations were used for training the SVM. For conditional GAN the higher dimensional feature vectors were used for training the SVM which was evaluated on the synthetically generated test set. As expected for the higher dimensional features the results shown are similar to what was obtained when the synthetic samples were used for training. For 2D samples the high accuracy suggests its much easier to estimate simpler dimensional distribution than a higher dimensional complex distribution.