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An Investigation of the Relation Between Grapheme Embeddings and Pronunciation for Tacotron-based Systems
Thanks to this property, we show that grapheme embeddings learned by Tacotron models can be useful for tasks such as grapheme-to-phoneme conversion and control of the pronunciation in synthetic speech.
\'Evaluation objective de plongements pour la synth\`ese de parole guid\'ee par r\'eseaux de neurones (Objective evaluation of embeddings for speech synthesis guided by neural networks)
La qualit{\'e} des plongements est li{\'e}e {\`a} la t{\^a}che sp{\'e}cifique pour laquelle ils ont {\'e}t{\'e} entra{\^\i}n{\'e}s et l{'}{\'e}valuation de cette t{\^a}che peut {\^e}tre un proc{\'e}d{\'e} long et on{\'e}reux s{'}il y a besoin d{'}annotateurs humains.
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