  This paper presents a French text-to-speech synthesis system for the Blizzard
Challenge 2023. The challenge consists of two tasks: generating high-quality
speech from female speakers and generating speech that closely resembles
specific individuals. Regarding the competition data, we conducted a screening
process to remove missing or erroneous text data. We organized all symbols
except for phonemes and eliminated symbols that had no pronunciation or zero
duration. Additionally, we added word boundary and start/end symbols to the
text, which we have found to improve speech quality based on our previous
experience. For the Spoke task, we performed data augmentation according to the
competition rules. We used an open-source G2P model to transcribe the French
texts into phonemes. As the G2P model uses the International Phonetic Alphabet
(IPA), we applied the same transcription process to the provided competition
data for standardization. However, due to compiler limitations in recognizing
special symbols from the IPA chart, we followed the rules to convert all
phonemes into the phonetic scheme used in the competition data. Finally, we
resampled all competition audio to a uniform sampling rate of 16 kHz. We
employed a VITS-based acoustic model with the hifigan vocoder. For the Spoke
task, we trained a multi-speaker model and incorporated speaker information
into the duration predictor, vocoder, and flow layers of the model. The
evaluation results of our system showed a quality MOS score of 3.6 for the Hub
task and 3.4 for the Spoke task, placing our system at an average level among
all participating teams.
