Neural Text-to-Speech Synthesis

Chapter 1. Introduction.- Part 1. Preliminary.- Chapter 2. Basics of Spoken Language Processing.- Chapter 3. Basics of Deep Learning.- Part 2. Key Components in TTS.- Chapter 4. Text Analyses.- Chapter 5. Acoustic Models.- Chapter 6. Vocoders.- Chapter 7. Fully End-to-End TTS.- Part 3. Advanced Topics in TTS.- Chapter 8. Expressive and Controllable TTS.- Chapter 9. Robust TTS.- Chapter 10. Model-Efficient TTS.- Chapter 11. Data-Efficient TTS.- Chapter 12. Beyond Text-to-Speech Synthesis.- Part 4. Summary and Outlook.- Chapter 13. Summary and Outlook.

Xu Tan is a Principal Researcher and Research Manager at Microsoft Research Asia. His research interests cover deep learning and its applications in language/speech/music processing and digital human creation. He has rich research experience in text-to-speech synthesis. He has developed high-quality TTS systems such as FastSpeech 1/2 (widely used in the TTS community), DelightfulTTS (winning the champion of the Blizzard TTS Challenge), and NaturalSpeech (achieving human-level quality on the TTS benchmark dataset), and transferred many research works to improve the experience of Microsoft Azure TTS services. He has given a series of tutorials on TTS at top conferences such as IJCAI, ICASSP, and INTERSPEECH, and written a comprehensive survey paper on TTS.

Besides speech synthesis, he has designed several popular language models (e.g., MASS) and AI music systems (e.g., Muzic), developed machine translation systems that achieved human parity in Chinese-English translation and won several champions in WMT machine translation competitions. He has published over 100 papers at prestigious conferences such as ICML, NeurIPS, ICLR, AAAI, IJCAI, ACL, EMNLP, NAACL, ICASSP, INTERSPEECH, KDD, and IEEE/ACM Transactions, and served as the area chair or action editor of some AI conferences and journals (e.g., NeurIPS, AAAI, ICASSP, TMLR).