Editing State-of-the-art (section)

==== Staib, M., Teh, T. H., Torresquintero, A., Mohan, D. S. R., Foglianti, L., Lenain, R., & Gao, J. (2020). Phonological features for 0-shot multilingual speech synthesis. ''arXiv preprint arXiv:2008.04107''. ====

* Summary: This article primarily aims to utilize a limited set of phonological features (PF), derived from the International Phonetic Alphabet (IPA), for achieving 0-shot speech synthesis and code-switching within a monolingual model. Specifically, the study selects Tacotron 2 as the baseline for comparison against methods of random initialization (RANDOM), manual mapping (MANUAL), and the PF-based approach (AUTO) proposed in this work. The conclusion drawn is that the speech generated using the AUTO method is more comprehensible.
* RQ: The research question of this paper explores whether phonological features (PF) can facilitate speech synthesis for languages not seen during training. Additionally, it examines whether PF can facilitate code-switched speech synthesis.
* Hypothesis: The hypothesis of the article is that phonological features (PFs), derived from the International Phonetic Alphabet (IPA), can enable 0-shot text-to-speech (TTS) synthesis and code-switching in languages that are not seen during training, even within monolingual models.
* Conclusion: The conclusion of the article is that by replacing the character input in Tacotron 2 with phonological features, a model topology can be created that is language-independent and allows for the automatic approximation of sounds unseen in training. The study shows that phonological features (PFs) can not only facilitate zero-shot speech synthesis in untrained languages within a small multilingual or even a monolingual model but also facilitate the synthesis of sounds that completely unseen in training. This suggests potential applications in code-switching and TTS for low-resource languages.
* Critical observations: This article mainly addresses the problem of 0-shot speech synthesis and code switching by using phonological features (PF). The significant advantage of this method is that it can reduce the amount of data for training multi-language speech synthesis models, and it is very helpful in low-resource languages and code-switched TTS. But PF may not capture all the differences of a language, especially for those with unique phonetic and phonological features, and the selected PFs might not adequately represent these languages. In addition, this article focuses more on generating understandable speech and may ignore the importance of features such as prosody.
* Relevance: This paper is mainly related to the fileds of cross-language speech synthesis and code switching speech synthesis. Some other studies have also proposed to find a unified representation (such as Unicode) to replace phoneme or text to achieve cross-language synthesis, but the PF proposed in this paper may be A better choice because these features retain speech features to a certain extent and help the model learn better.