Robust estimation of HMM parameters using fuzzy vector quantization and Parzen's window

Dai, Jianing

doi:10.1016/0031-3203(94)00075-w

Pattern Recognition

1995

DOI: 10.1016/0031-3203(94)00075-w

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Robust estimation of HMM parameters using fuzzy vector quantization and Parzen's window

Jianing Dai

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Cited by 3 publications

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Training hidden Markov models with multiple observations-a combinatorial method

Li¹,

Parizeau

Plamondon

2000

IEEE Trans. Pattern Anal. Machine Intell.

119

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ÐHidden Markov models (HMMs) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and versatility in handling sequential signals. On the other hand, as these models have a complex structure and also because the involved data sets usually contain uncertainty, it is difficult to analyze the multiple observation training problem without certain assumptions. For many years researchers have used Levinson's training equations in speech and handwriting applications, simply assuming that all observations are independent of each other. This paper presents a formal treatment of HMM multiple observation training without imposing the above assumption. In this treatment, the multiple observation probability is expressed as a combination of individual observation probabilities without losing generality. This combinatorial method gives one more freedom in making different dependence-independence assumptions. By generalizing Baum's auxiliary function into this framework and building up an associated objective function using the Lagrange multiplier method, it is proven that the derived training equations guarantee the maximization of the objective function. Furthermore, we show that Levinson's training equations can be easily derived as a special case in this treatment. Index TermsÐHidden Markov model, forward-backward procedure, Baum-Welch algorithm, multiple observation training.

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Training hidden Markov models with multiple observations-a combinatorial method

Li¹,

Parizeau

Plamondon

2000

IEEE Trans. Pattern Anal. Machine Intell.

119

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Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model

Cheng

2012

IEEE Trans. Fuzzy Syst.

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Reliable Method for Driving Events Recognition

Mitrović¹

2005

IEEE Trans. Intell. Transport. Syst.

145

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Robust estimation of HMM parameters using fuzzy vector quantization and Parzen's window

Cited by 3 publications

References 7 publications

Training hidden Markov models with multiple observations-a combinatorial method

Training hidden Markov models with multiple observations-a combinatorial method

Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model

Reliable Method for Driving Events Recognition

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