Advertisement

Learning with Segment Boundaries for Hierarchical HMMs

  • Naoto Gotou
  • Akira Hayashi
  • Nobuo Suematu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3686)

Abstract

Hierarchical hidden Markov models (HHMMs) can be used for time series segmentation. However, it is difficult to obtain a desirable segmentation result, because the form of learning for HHMMs is unsupervised. In the paper, we present a semisupervised learning algorithm for HHMMs. It is semisupervised in the sense that the supervisor teaches segmentation boundaries but not segment labels. The learning performance of the proposed algorithm is demonstrated through an experiment using music data.

Keywords

Hide Markov Model Segmentation Result Segment Boundary Horizontal Transition Hide Markov Model Parameter 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: analysis and applications. Machine Learning 32, 41–62 (1998)zbMATHCrossRefGoogle Scholar
  2. 2.
    Cohen, I., Garg, A., Huang, T.S.: Emotion recognition from facial expressions using multilevel HMM. In: Neural Information Processing Systems Workshop on Affective Computing, Denver, USA (2000)Google Scholar
  3. 3.
    Murphy, K.P., Paskin, M.A.: Hierarchical HMMs, Technical reports (2002)Google Scholar
  4. 4.
    Rabiner, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall PTR., Englewood Cliffs (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Naoto Gotou
    • 1
  • Akira Hayashi
    • 1
  • Nobuo Suematu
    • 1
  1. 1.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan

Personalised recommendations