Paper Info
Title
N-Way Composition Of Weighted Finite-State Transducers
Abstract
Composition of weighted transducers is a fundamental algorithm used in many applications, including for computing complex edit-distances between automata, or string kernels in machine learning, or to combine different components of a speech recognition, speech synthesis, or information extraction system. We present a generalization of the composition of weighted transducers, n-w ay composition, which is dramatically faster in practice than the standard composition algorithm when combining more than two transducers. The worst-case complexity of our algorithm for composing three transducers T(1), T(2), and T(3) resulting in T, is O(vertical bar T vertical bar(Q) min(d(T(1))d(T(3)), d(T(2))) + vertical bar T vertical bar(E)), where vertical bar.vertical bar(Q) denotes the number of states, vertical bar.vertical bar(E) the number of transitions, and d(.) the maximum out-degree. As in regular composition, the use of perfect hashing requires a pre-processing step with linear-time expected complexity in the size of the input transducers. In many cases, this approach significantly improves on the complexity of standard composition. Our algorithm also leads to a dramatically faster composition in practice. Furthermore, standard composition can be obtained as a special case of our algorithm. We report the results of several experiments demonstrating this improvement. These theoretical and empirical improvements significantly enhance performance in the applications already mentioned.
Year
DOI
Venue
2009
10.1142/S0129054109006772
INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE
Keywords
Field
DocType
speech recognition,edit distance,machine learning,information extraction,computational complexity,string kernel
Transducer,Discrete mathematics,Speech synthesis,Combinatorics,Automaton,Algorithm,Finite state,Information extraction,Perfect hash function,Mathematics,Special case
Journal
Volume
Issue
ISSN
20
4
0129-0541
Citations 
PageRank 
References 
6
0.52
6
Authors
2
Name
Order
Citations
PageRank
Cyril Allauzen169047.64
Mehryar Mohri24502448.21