Editing State-of-the-art (section)

===== Kong, J., Kim, J., & Bae, J. (2020). Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. ''Advances in neural information processing systems'', ''33'', 17022-17033. =====

* Summary: This article introduces HiFi-GAN, a model that can efficiently synthesize high-quality speech audio. HiFi-GAN consists of a generator and two discriminators: multi-scale discriminator and multi-period discriminator. Improve training stability and model performance by adversarially training the generator and discriminator and using two additional loss functions.

* RQ:Can HiFi-GAN effectively synthesize high-quality speech audio with computational efficiency comparable to human-level synthesis, while also demonstrating generalization across speakers and adaptability to various configurations?
* Hypothesis:By leveraging the characteristic patterns of speech audio and designing a discriminator to capture these patterns effectively, it is possible to develop a speech synthesis model, HiFi-GAN, that outperforms existing models in terms of synthesis quality and speed.
* Conclusion:HiFi-GAN significantly advances speech synthesis by efficiently generating high-quality audio, surpassing existing models in both synthesis quality and speed. By leveraging speech audio patterns and a carefully designed discriminator, this model demonstrates robustness across various scenarios, including unseen speakers and noisy inputs, while offering potential for on-device natural speech synthesis with low latency and memory requirements. Additionally, the flexibility of generator configurations enhances adaptability without the need for extensive hyper-parameter search.
* Critical observations:Due to the wide application of HiFi-GAN technology in the field of speech synthesis, there may be some ethical or social impacts, including concerns related to voice cloning, privacy and false information.
* Relevance:This paper is closely related to the topic of non-language-specific text-to-speech, as it demonstrates a breakthrough in HiFi-GAN models in synthesizing high-quality speech, with generalization capabilities, and the ability to handle inputs of different languages and speaking styles.