Graphical Analysis of Hidden Markov Model Speech Recognition Experiments.

Abstract

Hidden Markov models are powerful tools for acoustic modeling III speech recognition systems. However, detailed analysis of their performance in specific experiments can be difficult. Two tools were developed and implemented for the purpose of analyzing hidden Markov model experiments: an interactive Viterbi backtrace viewer and a multidimensional scaling display. These tools were built using the HMM Toolkit. Use of the Viterbi backtrace tool provided insight that eventually led to improved recognition performance. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 1995
Accession Number
ADA300991

Entities

People

  • D. C. Seward
  • M. A. Zissman

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Automated Speech Recognition
  • Classification
  • Data Analysis
  • Decoding
  • Department Of Defense
  • False Alarms
  • Hidden Markov Models
  • Markov Models
  • Models
  • Probability
  • Probability Density Functions
  • Recognition
  • Standards
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Auditory Neuroscience/Auditory Physiology.
  • Database Systems and Applications
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML