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Hidden Markov models for speech and signal recognition

R C Rose1, B H Juang

  • 1AT&T Bell Laboratories, Murray Hill, NJ, USA.

Electroencephalography and Clinical Neurophysiology. Supplement
|January 1, 1996
PubMed
Summary

Hidden Markov models (HMMs) are powerful statistical tools for speech recognition and signal modeling. This paper explores HMMs, their applications in speech, and potential uses in modeling biological waveforms like EEG.

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Area of Science:

  • Signal Processing
  • Statistical Modeling
  • Machine Learning

Background:

  • Hidden Markov Models (HMMs) are widely used for speech recognition and modeling.
  • HMMs utilize parametric statistical models comprising a Markov chain and output distributions.
  • These models capture the evolution of non-stationary processes, like speech, through hidden states.

Purpose of the Study:

  • To describe Hidden Markov Models (HMMs) as a general signal modeling procedure.
  • To detail the application of HMMs in speech recognition and modeling.
  • To explore other successful applications of HMMs and inspire new uses, particularly for biological waveform modeling.

Main Methods:

  • Description of Hidden Markov Models (HMMs) as parametric statistical models.
  • Explanation of the two-component structure: Markov chain for state sequences and output distributions for observations.
  • Review of HMM applications in speech recognition and other fields.

Main Results:

  • HMMs are established as a leading technique for speech recognition and modeling.
  • HMMs have demonstrated success in various signal processing and statistical modeling tasks.
  • The paper highlights the potential of HMMs for modeling continuous biological waveforms.

Conclusions:

  • Hidden Markov Models (HMMs) provide a robust framework for signal modeling and analysis.
  • The versatility of HMMs extends beyond speech to diverse applications, including biological signal processing.
  • Further research into HMMs for continuous waveform analysis, such as EEG, is encouraged.

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