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==== Synthesis ==== To summarize, text-to-speech research in recent years has explored multilingual data strategies, phonological features, and transfer learning methods to enhance its performance, especially for low-resource languages. Based on the studies reported above, multilingual models outperform monolingual ones, showing promise in improving the voice quality with limited data. Moreover, phonological features facilitate zero-shot synthesis and code-switching, benefiting LRLs and cross-language applications. Transfer learning methods like PHOIBLE-based phone mapping and phonological feature inputs improve output quality, with ASPF effective for source language selection. Finally, fine-tuning phonological representations enhances speech naturalness, suggesting the potential for multilingual g2p systems. These findings emphasise innovative approaches' importance in advancing TTS, with a specific focus on LRLs, offering insights into effective strategies and criteria for synthesis quality and scalability.
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