Editing State-of-the-art (section)

==== Do, P., Coler, M., Dijkstra, J., & Klabbers, E. (2023). Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection. ''arXiv preprint arXiv:2306.12040''. ====

*Summary: This paper compares two methods in TTS for low-resource languages: PHOIBLE-based phone mapping and phonological features input. Various languages are tested to see how these methods work across different languages. The findings show that both methods improve speech quality, with phonological features performing better. The study also examines two criteria for choosing source languages: Angular Similarity of Phone Frequencies (ASPF) and language family tree distance. ASPF is found effective, especially with phone-based input, while the language distance criterion does not yield expected results.
* RQ: The paper aims to explore how to most effectively deal with the input mismatch between languages and how to select the best source language to improve output quality in TTS for low-resource languages.
* Hypothesis: 
*# Transfer learning using PHOIBLE-based phone mapping and phonological feature inputs can improve TTS output quality for low-resource languages.
*# Angular Similarity of Phone Frequencies (ASPF) is an effective criterion for selecting source languages, more so than traditional broad language family classification.
* Conclusion: 
*# Both phone mapping and feature inputs can enhance output quality, with feature inputs showing better performance, although the effectiveness depends on the specific language pairing.
*# ASPF is effective in selecting source languages, especially when using label-based phone inputs, while the distance based on the language family tree does not work as expected.
* Critical observations:
*# Although ASPF is effective in some cases, its effectiveness is not universal across all language combinations, indicating the need for further research to understand influencing factors.
*# The unexpected results with the language family tree distance suggest that there might be unidentified factors at play, necessitating further investigation.
* Relevance: This research is significant for the development of TTS technology for low-resource languages, especially in offering new insights into source language selection and handling input mismatches between languages. Moreover, the proposed methods are important for the multilingual applicability and scalability of speech technologies.