  The SynPaFlex-Corpus is a publicly available TTS-oriented dataset, which
provides phonetic transcriptions automatically produced by the JTrans
transcriber, with a Phoneme Error Rate (PER) of 6.1%. In this paper, we analyze
two mono-speaker Tacotron2 models trained on graphemic and phonetic inputs,
provided by the SynPaFlex-Corpus. Through three subjective listening tests, we
compare their pronunciation accuracy, sound quality and naturalness. Our
results show significantly better pronunciation accuracy and prosody
naturalness for the phoneme-based model, but no significant difference in terms
of perceived sound quality. They demonstrate that a PER of 6.1% is sufficient
to enhance pronunciation control by using phonetic transcripts instead of
graphemes with 83 hours of recorded French read speech. They suggest that the
SynPaFlex-Corpus is suitable for pre-training a model in mono-speaker
fine-tuning approaches.
