We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves ...

We implement the following features in this framework: Data processing for non-autoregressive Text-to-Speech using Montreal Forced Aligner. Convenient and scalable framework for training and inference ...

To determine how listeners learn the statistical properties of acoustic spaces, we assessed their ability to perceive speech in a range of noisy and reverberant rooms. Listeners were also exposed to ...

Our ECoG to Speech decoding framework is initially described in A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis. We present a novel deep learning-based neural speech ...

The novel deep learning-based time domain single channel speech source separation methods have shown remarkable progress. Recent studies achieve either successful global or local context modeling for ...