Experiments with Grand Variance in the Arm Continuous Speech Recognition System

Abstract

The use of triphones to cope with contextual effects in phoneme-level hidden Markov model (HMM) based speech recognition results in a huge increase in the number of system parameters which need to be estimated. The solution to this problem is to reduce the number of independent system parameters so that those which remain can be estimated more robustly from the training data. For HMMs with Gaussian state output probability density functions (pdfs), a simple example of such an approach is the 'grand' variance method in which all state output pdfs share the same covariance matrix. This paper reports the results of experiments designed to investigate the effect of grand variance on the performance of the triphone-HMM based ARM continuous speech recognition system. (JHD)

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Document Details

Document Type
Technical Report
Publication Date
Feb 08, 1990
Accession Number
ADA221728

Entities

People

  • K. M. Ponting
  • M. J. Russell

Organizations

  • Royal Signals and Radar Establishment

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Automated Speech Recognition
  • Classification
  • Covariance
  • Dictionaries
  • Dynamic Programming
  • Foreign Languages
  • Hidden Markov Models
  • Markov Models
  • Models
  • Probability
  • Recognition
  • Test Sets
  • Training
  • Vocabulary
  • Word Recognition

Fields of Study

  • Computer science
  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Speech Processing/Speech Recognition.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms