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Empirical Assessment of Two Strategies for Optimizing the Viterbi Algorithm

  • Roberto Esposito
  • Daniele P. Radicioni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5883)

Abstract

The Viterbi algorithm is widely used to evaluate sequential classifiers. Unfortunately, depending on the number of labels involved, its time complexity can still be too high for practical purposes. In this paper, we empirically compare two approaches to the optimization of the Viterbi algorithm: Viterbi Beam Search and CarpeDiem. The algorithms are illustrated and tested on datasets representative of a wide range of experimental conditions. Results are reported and the conditions favourable to the characteristics of each approach are discussed.

Keywords

Beam Size Viterbi Algorithm Markov Assumption Continuous Speech Recognition Perceptron Algorithm 
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 2009

Authors and Affiliations

  • Roberto Esposito
    • 1
  • Daniele P. Radicioni
    • 1
  1. 1.Dipartimento di InformaticaUniversità di Torino 

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