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In this paper, we describe the systems developed by the SJTU X-LANCE team forLIMMITS 2023 Challenge, and we mainly focus on the winning system onnaturalness for track 1. The aim of this challenge is to build a multi-speakermulti-lingual text-to-speech (TTS) system for Marathi, Hindi and Telugu. Eachof the languages has a male and a female speaker in the given dataset. In track1, only 5 hours data from each speaker can be selected to train the TTS model.Our system is based on the recently proposed VQTTS that utilizes VQ acousticfeature rather than mel-spectrogram. We introduce additional speaker embeddingsand language embeddings to VQTTS for controlling the speaker and languageinformation. In the cross-lingual evaluations where we need to synthesizespeech in a cross-lingual speaker's voice, we provide a native speaker'sembedding to the acoustic model and the target speaker's embedding to thevocoder. In the subjective MOS listening test on naturalness, our systemachieves 4.77 which ranks first.

Multi-Speaker Multi-Lingual VQTTS System for LIMMITS 2023 Challenge