ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks
Table 1: FAD and naturalness MOS with 95% confidence intervals
['Metric', 'Method', 'IEMOCAP Angry', 'IEMOCAP Happy', 'IEMOCAP Sad', 'IEMOCAP Neutral', 'MEmoSD Angry', 'MEmoSD Happy', 'MEmoSD Sad', 'MEmoSD Neutral']
It can be noted that the FADs on IEMOCAP is higher than that of MEmoSD. The reason is that the IEMOCAP dataset contains a lot of noise while MEmoSD is recorded in the lab environments. Thus the performance is better with cleaner data. It is obvious that the emotional speech synthesized by ET-GAN is better than other baseline methods in naturalness. The experimental results demonstrate that ET-GAN has better performance in emotion correctness and speaker similarity with a high level of control of emotion information.