Invited Speakers

Aäron van den Oord (Google DeepMind, UK)

Deep learning for speech synthesis

Claire Gardent (CNRS, France)

Natrual language generation and speech synthesis

Tecumseh Fitch (University of Vienna, Austria), Bart de Boer (Vrije Universiteit Brussel, Belgium)

Synthesizing animal vocalizations and modelling animal speech

In the last two decades, theory from speech science and methods from digital signal processing have been productively used to study animal communication in many different ways. This has led to fundamental advances in our understanding of how animals produce and perceive their vocalizations, and use them to communicate with one another. A central insight was that the source-filter theory of vocal production, initially developed in speech science, applies to most vertebrate vocal systems as well. This opened the door to using methods like linear prediction to analyze source and filter characteristics, and to re-synthesize realistic vocalizations with precise changes to fundamental frequency, formants and other characteristics. We give an overview of this progress, with several specific examples from our own work covered in more detail.