This paper presents the development of a speech synthesis system for the
LIMMITS'24 Challenge, focusing primarily on Track 2. The objective of the
challenge is to establish a multi-speaker, multi-lingual Indic Text-to-Speech
system with voice cloning capabilities, covering seven Indian languages with
both male and female speakers. The system was trained using challenge data and
fine-tuned for few-shot voice cloning on target speakers. Evaluation included
both mono-lingual and cross-lingual synthesis across all seven languages, with
subjective tests assessing naturalness and speaker similarity. Our system uses
the VITS2 architecture, augmented with a multi-lingual ID and a BERT model to
enhance contextual language comprehension. In Track 1, where no additional data
usage was permitted, our model achieved a Speaker Similarity score of 4.02. In
Track 2, which allowed the use of extra data, it attained a Speaker Similarity
score of 4.17.

本文介绍了一个语音合成系统的开发，用于 LIMMITS'24 挑战赛，主要关注第二阶段。该系统的目标是建立一个具有语音克隆功能的多说话人、多语言的印度文本到语音系统，涵盖了七种印度语言和男女两性的发言人。该系统通过使用挑战数据进行训练，并针对目标发言人进行少样本语音克隆进行了微调。评估包括对所有七种语言进行单语和跨语言综合的主观测试，评估自然度和发言人相似性。我们的系统使用了 VITS2 架构，结合多语言 ID 和 BERT 模型来增强上下文语言理解。在第一阶段，不允许使用额外数据的情况下，我们的模型的发言人相似性得分为 4.02。而在允许使用额外数据的第二阶段，得分为 4.17。