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ABSTRACT

The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstrate some limitations of the original HDP-HMM formulation (Teh et al., 2006), and propose a sticky extension which allows more robust learning of smoothly varying dynamics. Using DP mixtures, this formulation also allows learning of more complex, multimodal emission distributions. We further develop a sampling algorithm that employs a truncated approximation of the DP to jointly resample the full state sequence, greatly improving mixing rates. Via extensive experiments with synthetic data and the NIST speaker diarization database, we demonstrate the advantages of our sticky extension, and the utility of the HDP-HMM in real-world applications.

REFERENCES

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• 2: Emily B. Fox , Erik B. Sudderth , Michael I. Jordan , Alan S. Willsky, An HDP-HMM for systems with state persistence, Proceedings of the 25th international conference on Machine learning, p.312-319, July 05-09, 2008, Helsinki, Finland [doi>10.1145/1390156.1390196]
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• 7: Rodriguez, A., Dunson, D., & Gelfand, A. (2006). The nested Dirichlet process. Duke ISDS, TR #06--19.
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• 10: Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical Dirichlet processes. J. Amer. Stat. Assoc., 101, 1566--1581.
• 11: Wooters, C., & Huijbregts, M. (2007). The ICSI RT07s speaker diarization system. To appear in LNCS.
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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Markov processes

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.2 Approximation
  G.3 PROBABILITY AND STATISTICS
      Subjects: Nonparametric statistics

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Design, Theory