Description
Zoneout is a method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain their previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks.
Papers Using This Method
Training Universal Vocoders with Feature Smoothing-Based Augmentation Methods for High-Quality TTS Systems2024-09-04Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning2024-05-22An overview of text-to-speech systems and media applications2023-10-22Energy-Based Models For Speech Synthesis2023-10-19Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration2023-05-25ArmanTTS single-speaker Persian dataset2023-04-07Facial Landmark Predictions with Applications to Metaverse2022-09-29ZoDIAC: Zoneout Dropout Injection Attention Calculation2022-06-28Zero-Shot Long-Form Voice Cloning with Dynamic Convolution Attention2022-01-25ITAcotron 2: Transfering English Speech Synthesis Architectures and Speech Features to Italian2021-11-01Neural Sequence-to-Sequence Speech Synthesis Using a Hidden Semi-Markov Model Based Structured Attention Mechanism2021-08-31Neural HMMs are all you need (for high-quality attention-free TTS)2021-08-30Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis2021-06-15VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention2021-02-12Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech2021-01-01Using previous acoustic context to improve Text-to-Speech synthesis2020-12-07Learning Speaker Embedding from Text-to-Speech2020-10-21Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling2020-10-08SpeedySpeech: Efficient Neural Speech Synthesis2020-08-09One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech2020-08-03