Distances between Distributions: Comparing Language Models

  • Thierry Murgue
  • Colin de la Higuera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

Language models are used in a variety of fields in order to support other tasks: classification, next-symbol prediction, pattern analysis. In order to compare language models, or to measure the quality of an acquired model with respect to an empirical distribution, or to evaluate the progress of a learning process, we propose to use distances based on the L 2 norm, or quadratic distances. We prove that these distances can not only be estimated through sampling, but can be effectively computed when both distributions are represented by stochastic deterministic finite automata. We provide a set of experiments showing a fast convergence of the distance through sampling and a good scalability, enabling us to use this distance to decide if two distributions are equal when only samples are provided, or to classify texts.

Keywords

Speech Recognition Language Model Automatic Speech Recognition Finite Automaton Deterministic Finite Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Thierry Murgue
    • 1
    • 2
  • Colin de la Higuera
    • 2
  1. 1.RIMEcole des Mines de Saint-EtienneSaint-Etienne cedex 2France
  2. 2.EURISEUniversity of Saint-EtienneSaint-Etienne cedex 2France

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