Editing State-of-the-art (section)

==== Wells D, Richmond K. Cross-lingual transfer of phonological features for low-resource speech synthesis[C]//Proceedings of the 11th Speech Synthesis Workshop, Budapest, Hungary. 2021: 160-165. ====

* Summary: In this paper, researchers compare two methods: fine-tuning phonemic representations and using phonological features. They used SPE-style phonological features, offering a binary representation of phonemes, which helps describe and analyze speech patterns in English and German. The study discovers that even with limited target language data, fine-tuning can generate speech comparable to models trained from scratch. Using phonological features slightly improves naturalness ratings compared to using phonemes alone. These findings highlight the practical benefits of phonological features in improving TTS output quality across languages.
* RQ: Does the use of different input representations (phonemes and phonological features) affect the naturalness of synthesized speech in text-to-speech synthesis using cross-lingual transfer learning?
* Hypothesis: In cross-lingual transfer learning for text-to-speech synthesis, the use of different input representations (phonemes and phonological features) affects the naturalness of synthesized speech.
* Conclusion: The study confirmed the effectiveness of cross-lingual fine-tuning for training synthetic voices with limited target language data. Phonological features were found to offer practical benefits over phonemes in terms of parameter sharing during transfer learning.
* Critical observations: There was a slight improvement in naturalness ratings when using PFs over phonemes. Future research may explore multilingual grapheme-to-phoneme systems and utilize additional linguistic resources to enhance low-resource pipelines for text-to-speech synthesis
* Relevance: Phonological features were found to offer practical benefits over phonemes in terms of parameter sharing during transfer learning, which can be applied greatly in LRLs TTS.