Liaison and pronunciation learning in end-to-end text-to-speech in French

Sequence-to-sequence (S2S) TTS models like Tacotron have grapheme-only inputs when trained fully end-to-end. Grapheme inputs map to phone sounds depending on context, which traditionally is handled by extensive preprocessing in the TTS front-end. However, French orthography does not provide a clear one-to-one mapping between graphemes and sounds, and in English, which similarly has rather non-phonetic orthography, pronunciations are a significant cause of error in S2S- TTS with grapheme-inputs. In this paper, we test implicit pronunciation knowledge where graphemes do not map directly to phones. Implicit pronunciation knowledge learnt in S2S-TTS is similar to a standalone grapheme-to-phoneme (G2P) model, which makes explicit phone predictions at the sequential level. We find grapheme-input S2S-TTS makes implicit pronunciation errors similar to explicit G2P models - notably for foreign names. In a traditional front-end pipeline, there are also post-lexical rules which modify G2P output at the sequential level. In French, post-lexical rules require a deep knowledge of linguistic structure in a process called Liaison. Without explicit rules, we find S2S-TTS with grapheme-inputs over-inserts Liaison sounds, leading to a significant preference for a phone-based equivalent. By testing with linguistically-motivated stimuli, we observe differences that would otherwise go undetected.