On Enhancing Speech Emotion Recognition using Generative Adversarial Networks
Table 3: Classification results of a SVM trained on synthetic samples along with real training samples for different scenarios for cross-corpus experiments
['Dataset', 'UAR (%)']
Having studied the convergence of GAN architectures and evaluating the quality of synthetically generated samples produced by them in a single corpora setting, we now move to performing cross-corpus evaluations. The objective of this experiment is to investigate if synthetically generated samples can be used during classification on an external corpus (as opposed to being applicable for only in-domain tasks). MSP-IMPROV, like IEMOCAP, also has actors participating in dyadic conversations which has then been segmented into utterances and annotated by evaluators. There are 7798 utterances in total spanned across the same four emotion classes. However, the distribution across classes was highly unbalanced with the number of utterances belonging to happy/neutral class more than three times the number of angry/sad samples. This prompted us to use it as a test set rather than training set. We observe that the adversarial errors converge even if the test set is a different corpus than the training set.