AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines

TL;DR

This paper introduces AISHELL-3, a large-scale multi-speaker Mandarin speech corpus with detailed speaker attributes, and presents a baseline multi-speaker TTS system capable of zero-shot voice cloning, demonstrating high voice similarity.

Contribution

The paper provides a new high-quality Mandarin speech dataset with detailed speaker info and develops a multi-speaker TTS baseline system with zero-shot capabilities.

Findings

High voice similarity in synthesis results

Effective zero-shot voice cloning demonstrated

Dataset and baseline system publicly available

Abstract

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.