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Related Experiment Video

Updated: May 29, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Experiments in text recognition with the modified viterbi algorithm.

R Shinghal1, G T Toussaint

  • 1MEMBER, IEEE, Department of Computer Science, Concordia University, Montreal, P.Q., Canada.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Viterbi algorithm with a heuristic search for improved efficiency in character-based language processing. Experiments show the benefits of using word-length and position-dependent transition probabilities.

Related Experiment Videos

Last Updated: May 29, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states.
  • Its application in areas like speech recognition and bioinformatics is widespread.
  • Computational complexity can be a limiting factor in large-scale applications.

Purpose of the Study:

  • To introduce a modified Viterbi algorithm incorporating a heuristic search strategy.
  • To analyze the computational complexity of the modified algorithm.
  • To empirically evaluate the effectiveness of using word-length and position-dependent transition probabilities in character-based language models.

Main Methods:

  • Formal description of a modified Viterbi algorithm utilizing a heuristic search.
  • Derivation of a complexity measure for the modified algorithm.
  • Exhaustive experimentation on machine-printed text data.
  • Modeling language as a Markov chain with character transition probabilities.

Main Results:

  • The modified Viterbi algorithm demonstrates a reduced search space through its heuristic.
  • Empirical results provide insights into the benefits of context-aware transition probabilities.
  • The study quantifies the advantage of using transition probabilities that vary with word length and position.

Conclusions:

  • The heuristic modification offers a practical approach to managing Viterbi algorithm complexity.
  • Context-dependent transition probabilities significantly enhance performance in character-level language modeling.
  • This research provides empirical evidence for optimizing language processing models.