Period VITS: Variational Inference With Explicit Pitch Modeling For End-to-End Emotional Speech Synthesis Yuma Shirahata, Ryuichi Yamamoto, Eunwoo Song, Ryo Terashima, Jae-Min Kim, Kentaro Tachibana Oct 18, 2022 Go to Project Site Deep Learning End-to-End TTS Ryuichi Yamamoto Engineer/Researcher I am a software engineer / researcher passionate about speech synthesis. I love to write code and enjoy open-source collaboration on GitHub. Please feel free to reach out on Twitter and GitHub. Related Lightweight and High-Fidelity End-to-End Text-to-Speech with Multi-Band Generation and Inverse Short-Time Fourier Transform DRSpeech: Degradation-Robust Text-to-Speech Synthesis with Frame-Level and Utterance-Level Acoustic Representation Learning TTS-by-TTS 2: Data-selective Augmentation for Neural Speech Synthesis Using Ranking Support Vector Machine with Variational Autoencoder Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation Language Model-Based Emotion Prediction Methods for Emotional Speech Synthesis Systems