Towards Zero-Shot Multi-Speaker Multi-Accent Text-to-Speech Synthesis

This letter presents a framework towards multi-accent neural text-to-speech synthesis for zero-shot multi-speaker, which employs an encoder-decoder architecture and an accent classifier to control the pronunciation variation from the encoder. The encoder and decoder are pre-trained on a large-scale multi-speaker corpus. The accent-informed encoder outputs are taken by the attention-based decoder to generate accented prosody. This framework allows for fine-tuning with limited training data from multiple accents, and is able to generate accented speech for unseen speakers. Both objective and subjective evaluations confirm the effectiveness of the proposed framework.