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This paper presents a French text-to-speech synthesis system for the BlizzardChallenge 2023. The challenge consists of two tasks: generating high-qualityspeech from female speakers and generating speech that closely resemblesspecific individuals. Regarding the competition data, we conducted a screeningprocess to remove missing or erroneous text data. We organized all symbolsexcept for phonemes and eliminated symbols that had no pronunciation or zeroduration. Additionally, we added word boundary and start/end symbols to thetext, which we have found to improve speech quality based on our previousexperience. For the Spoke task, we performed data augmentation according to thecompetition rules. We used an open-source G2P model to transcribe the Frenchtexts into phonemes. As the G2P model uses the International Phonetic Alphabet(IPA), we applied the same transcription process to the provided competitiondata for standardization. However, due to compiler limitations in recognizingspecial symbols from the IPA chart, we followed the rules to convert allphonemes into the phonetic scheme used in the competition data. Finally, weresampled all competition audio to a uniform sampling rate of 16 kHz. Weemployed a VITS-based acoustic model with the hifigan vocoder. For the Spoketask, we trained a multi-speaker model and incorporated speaker informationinto the duration predictor, vocoder, and flow layers of the model. Theevaluation results of our system showed a quality MOS score of 3.6 for the Hubtask and 3.4 for the Spoke task, placing our system at an average level amongall participating teams.

The FruitShell French synthesis system at the Blizzard 2023 Challenge