Editing State-of-the-art (section)

==== Y. Koizumi, H. Zen, S. Karita, et al. (2023). Miipher: A robust speech restoration model integrating self-supervised speech and text representations, arXiv:2303.01664. ====
*'''Summary:''' The paper presents Miipher, a robust speech restoration (SR) model that integrates self-supervised speech and text representations to enhance the quality of degraded speech signals. The model is designed to address two primary challenges in SR: phoneme masking and deletion.
* '''RQ:''' How to develop a robust speech restoration (SR) model that can convert degraded speech signals into high-quality ones, with a focus on handling difficult degradations such as phoneme masking and deletion?
* '''Hypothesis:''' The proposed SR model, Miipher, will be robust against various audio degradations and enable the training of high-quality text-to-speech (TTS) models from restored speech samples.
* '''Conclusion:''' The study concludes that Miipher is effective in restoring speech samples in-the-wild and can increase the value of speech samples by improving their quality as training data for speech generation tasks.
* '''Critical observations:''' The use of w2v-BERT features significantly improves SR performance compared to log-mel spectrogram-based methods, the effectiveness of PnG-BERT features in preserving text content, and the importance of speaker embeddings for retaining speaker characteristics in restored speech.
* '''Relevance:''' The relevance of this study is significant for the field of speech enhancement/restoration, as it demonstrates a method to enhance the quality of existing speech datasets and expand the potential applications of non-studio speech recordings.