nnSpeech: Speaker-Guided Conditional Variational Autoencoder for Zero-Shot Multi-speaker text-to-speech

Author:

Zhao Botao1,Zhang Xulong1,Wang Jianzong1,Cheng Ning1,Xiao Jing1

Affiliation:

1. Ping An Technology (Shenzhen) Co., Ltd.,China

Funder

Research and Development

Publisher

IEEE

Reference23 articles.

1. Neural voice cloning with a few samples;arik;Advances in neural information processing systems,2018

2. Bi-Level Speaker Supervision for One-Shot Speech Synthesis

3. Zero-Shot Multi-Speaker Text-To-Speech with State-Of-The-Art Neural Speaker Embeddings

4. Meta-stylespeech: Multi-speaker adaptive text-to-speech generation;min;ICML,2021

5. Transfer learning from speaker verification to multispeaker text-to-speech synthesis;jia;Advances in neural information processing systems,2018

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1. Fast Cross-Modality Knowledge Transfer via a Contextual Autoencoder Transformation;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

2. One-shot multi-speaker text-to-speech using RawNet3 speaker representation*;Phonetics and Speech Sciences;2024-03

3. USAT: A Universal Speaker-Adaptive Text-to-Speech Approach;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024

4. Optimizing Audio-Visual Speech Enhancement Using Multi-Level Distortion Measures for Audio-Visual Speech Recognition;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2024

5. SAR: Self-Supervised Anti-Distortion Representation for End-To-End Speech Model;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

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