Wei-Ning Hsu

TL;DR: The authors proposed an unsupervised autoregressive neural model for learning generic speech representations, which is designed to preserve information for a wide range of downstream tasks, such as phone classification and speaker verification.

TL;DR: Data2vec is a framework that uses the same learning method for either speech, NLP or computer vision to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.

TL;DR: HuBERT as mentioned in this paper utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss, which forces the model to learn a combined acoustic and language model over the continuous inputs.

TL;DR: A factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision by formulating it explicitly within a factorsized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables.

TL;DR: This document outlines the underlying design of Lingvo and serves as an introduction to the various pieces of the framework, while also offering examples of advanced features that showcase the capabilities of the Framework.