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Integrating Human Linguistic Insights into AI: Theory-Driven Representation for Multilingual Text-to-Speech
This paper explores the integration of human linguistic insights into multilingual text-to-speech (TTS) systems by evaluating the Featurally Underspecified Lexicon (FUL) as a theory-driven input representation. Unlike data-intensive end-to-end models, FUL offers a compact, interpretable feature set
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