ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks
Table 3: Ablation Study
['Method', 'FAD Angry', 'FAD Happy', 'FAD Sad', 'FAD Neutral', 'MOS Angry', 'MOS Happy', 'MOS Sad', 'MOS Neutral']
The ablation study is carried out on MEmoSD dataset for all the emotions. Both FAD and MOS are evaluated. Linguistic loss which restricts the distances between the spectrum is crucial for preserving the content of speech, thus adding it improves the baseline by a large margin. However, only by adding the speaker verification loss, the model tends to be difficult to converge without controlling linguistic information. The loss of semantic information leads to bad performance. However, by combining both the linguistic and the speaker loss, the network can leverage the advantages of each of them and lead to superior results. Further results show that GAN is able to perform with gradient clipping in a steadier way. But our proposed gradient penalty outperforms that of gradient clipping. Thus verified the effectiveness of gradient penalty. Overall, our final model performs the best by means of both objective and subjective. And each component of our proposed model can be coordinated to work together towards a stable and effective emotion transfer framework. Each part of them contributes to our final performance.