In this paper, we present AISHELL-3, a large-scale and high-fidelity
multi-speaker Mandarin speech corpus which could be used to train multi-speaker
Text-to-Speech (TTS) systems. The corpus contains roughly 85 hours of
emotion-neutral recordings spoken by 218 native Chinese mandarin speakers.
Their auxiliary attributes such as gender, age group and native accents are
explicitly marked and provided in the corpus. Accordingly, transcripts in
Chinese character-level and pinyin-level are provided along with the
recordings. We present a baseline system that uses AISHELL-3 for multi-speaker
Madarin speech synthesis. The multi-speaker speech synthesis system is an
extension on Tacotron-2 where a speaker verification model and a corresponding
loss regarding voice similarity are incorporated as the feedback constraint. We
aim to use the presented corpus to build a robust synthesis model that is able
to achieve zero-shot voice cloning. The system trained on this dataset also
generalizes well on speakers that are never seen in the training process.
Objective evaluation results from our experiments show that the proposed
multi-speaker synthesis system achieves high voice similarity concerning both
speaker embedding similarity and equal error rate measurement. The dataset,
baseline system code and generated samples are available online.

本论文介绍了 AISHELL-3 数据集，用于训练多扬声器普通话语音合成系统，同时介绍了一种基于 Tacotron-2 的多扬声器语音合成基线模型，该模型结合了说话人验证模型和相应的语音相似性损失作为反馈约束。该论文旨在利用所提供的语音数据集构建鲁棒的语音合成模型，并实现零 - shot 语音克隆。实验结果表明，该多说话人合成系统在声纹嵌入相似性和等误差率测量方面均能实现较高的语音相似度。