In this paper, we describe the systems developed by the SJTU X-LANCE team for LIMMITS 2023 Challenge, and we mainly focus on the winning system on naturalness for track 1. The aim of this challenge is to build a multi-speaker multi-lingual text-to-speech (TTS) system for Marathi, Hindi and Telugu. Each of the languages has a male and a female speaker in the given dataset. In track 1, 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 acoustic feature rather than mel-spectrogram. We introduce additional speaker embeddings and language embeddings to VQTTS for controlling the speaker and language information. In the cross-lingual evaluations where we need to synthesize speech in a cross-lingual speaker's voice, we provide a native speaker's embedding to the acoustic model and the target speaker's embedding to the vocoder. In the subjective MOS listening test on naturalness, our system achieves 4.77 which ranks first.

翻译：本文描述了SJTU X-LANCE团队为LIMMITS 2023挑战赛开发的系统，重点介绍了我们在赛道1自然度方面的获胜系统。该挑战赛的目标是构建一个面向马拉地语、印地语和泰卢固语的多说话人多语言文本转语音（TTS）系统。给定数据集中每种语言均包含一名男性和一名女性说话人。在赛道1中，每个说话人仅能选取5小时数据用于训练TTS模型。我们的系统基于近期提出的VQTTS，该模型利用VQ声学特征替代梅尔频谱图。我们向VQTTS引入额外的说话人嵌入和语言嵌入，以控制说话人与语言信息。在需要以跨语言说话人声音合成语音的跨语言评估中，我们为声学模型提供母语说话人嵌入，为声码器提供目标说话人嵌入。在自然度的主观MOS听力测试中，我们的系统取得4.77分，排名第一。