A minimum frame error criterion for hidden markov model training

Taemin Cho, Kibeom Kim, Juan P. Bello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hidden Markov models (HMM) have been widely studied and applied over decades. The standard supervised learning method for HMM is maximum likelihood estimation (MLE) which maximizes the joint probability of training data. However, the most natural way of training would be finding the parameters that directly minimize the error rate of a given training set. In this article, we propose a novel learning method that minimizes the number of incorrectly decoded labels frame-wise. To do this, we construct a smooth function that is arbitrarily close to the exact frame error rate and minimize it directly using a gradient-based optimization algorithm. The proposed approach is intuitive and simple. We applied our method to the task of chord recognition in music, and the results show that it performs better than Maximum Likelihood Estimation and Minimum Classification Error.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages363-368
Number of pages6
DOIs
StatePublished - Dec 1 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume2

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

Keywords

  • HMM
  • MFE
  • MLE
  • chord recognition
  • machine learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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  • Cite this

    Cho, T., Kim, K., & Bello, J. P. (2012). A minimum frame error criterion for hidden markov model training. In Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 (pp. 363-368). [6406763] (Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012; Vol. 2). https://doi.org/10.1109/ICMLA.2012.147