  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.
