Editing State-of-the-art (section)

==== Finkelstein, L., Zen, H., Casagrande, N., Chan, C., Jia, Y., Kenter, T., Petelin, A., Shen, J., Wan, V., Zhang, Y., Wu, Y., & Clark, R. (2022). Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks. Google LLC. Retrieved from <nowiki>https://arxiv.org/abs/2208.13183</nowiki> ====
'''Summary''': This paper presents a practical approach for accent transfer tasks in text-to-speech (TTS) synthesis, where aspects of one speaker's speech are transferred to another speaker's speech. The authors address the challenge of creating high-quality transfer models that are also stable and suitable for user-facing applications. They propose a two-step training process involving a Tacotron-based accent transfer model and a robust CHiVE-BERT TTS system. The CHiVE-BERT system is trained on synthetic data generated by the Tacotron model, which results in high-quality audio with transferred accents while preserving speaker characteristics.

'''RQ:''' How can text-to-speech systems be trained to achieve accent transfer effectively and stably, without compromising the quality or usability of the synthesized speech?

'''Hypothesis:''' By training a robust TTS system on synthetic data generated by a less stable but high-quality accent transfer model, it is possible to achieve a balance between quality and stability in accent transfer tasks.

'''Conclusion:''' The study concludes that the proposed two-step training approach, using synthetic data generated by a Tacotron-based model to train a CHiVE-BERT system, yields reliable performance in terms of naturalness and accent transfer capability. The quality loss associated with the switch to synthetic data is within acceptable bounds, and the final system produces high-quality audio that maintains the original speakers' characteristics.

'''Critical observations:''' The authors note that the quality of the final system is affected by the intermediate Tacotron model, with some accents showing significant quality loss, particularly for female speakers in British English. Training on synthetic data can result in lower quality loss compared to using human recordings, possibly due to the reduced variance in synthetic data. The choice of vocoder, synthesizer, and the balance between synthetic and human recordings are critical in the training process, with the final system benefiting from a combination of both.

'''Relevance:''' The research on accent transfer in TTS systems aligns closely with my focus on accent modification for Turkish immigrants in Dutch oral exams. The methodologies explored for synthesizing and transferring accents can be adapted to develop tools that neutralize accents, enhancing exam fairness by ensuring evaluations are based on language skills rather than accent.