Search Results for author:
Found 4 papers, 2 papers with code
RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis
This work introduces a predominantly parallel, end-to-end TTS model based on normalizing flows.
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
Probabilistic Latent Variable Models (LVMs) provide an alternative to self-supervised learning approaches for linguistic representation learning from speech.
Towards Using Context-Dependent Symbols in CTC Without State-Tying Decision Trees
The embedding network is trained together with the rest of the acoustic model and removes one of the last cases in which neural systems have to be bootstrapped from GMM-HMM ones.
Efficient Purely Convolutional Text Encoding
In this work, we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
