Weighted Automata in Text and Speech Processing

TL;DR

This paper discusses the use of weighted automata in text and speech processing, highlighting their theoretical foundations, algorithms for composition, and benefits of determinization and minimization.

Contribution

It introduces efficient algorithms for weighted automata, including composition, determinization, and minimization, enhancing their applicability in NLP and speech processing.

Findings

Efficient composition algorithm for weighted transducers

Demonstrated benefits of determinization and minimization

Improved performance in NLP and speech tasks

Abstract

Finite-state automata are a very effective tool in natural language processing. However, in a variety of applications and especially in speech precessing, it is necessary to consider more general machines in which arcs are assigned weights or costs. We briefly describe some of the main theoretical and algorithmic aspects of these machines. In particular, we describe an efficient composition algorithm for weighted transducers, and give examples illustrating the value of determinization and minimization algorithms for weighted automata.