An Introduction to Automatic Differentiation with Weighted Finite-State Automata
Weighted finite-state automata (WFSAs) have been a critical building block in modern automatic speech recognition. However, their use in conjunction with "end-to-end" deep learning systems is limited by the lack of efficient frameworks with support for automatic differentiation. This limitation is being overcome with the advent of new frameworks like GTN and k2. This tutorial will cover the basics of WFSAs and review their application in speech recognition. We will then explain the core concepts of automatic differentiation and show how to use it with WFSAs to rapidly experiment with new and existing algorithms. We will conclude with a discussion of the open challenges and opportunities for WFSAs to grow as a central component in automatic speech recognition and related applications. Awni Hannun Awni is a research scientist at the Facebook AI Research (FAIR) lab, focusing on low-resource machine learning, speech recognition, and privacy. He earned a Ph.D. in computer science from Stanford University. Prior to Facebook, he worked as a research scientist in Baidu's Silicon Valley AI Lab, where he co-led the Deep Speech projects.