Before revision:  Furthermore, we perform an analysis of the representation learned by the Tacotron model and show that the contextual grapheme embeddings encode phoneme information , and that they can be used for grapheme-to-phoneme conversion and phoneme control of synthetic speech.
After revision:  Furthermore, we perform an analysis of the representation learned by the model and word pronunciations. This work investigates this relation in the case of a Tacotron model trained on French graphemes. Our analysis shows that grapheme embeddings are related to phoneme information despite no such information being present during training. 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 phoneme control of synthetic speech.