Segment-Based Acoustic Models for Continuous Speech Recognition

Abstract

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which have high computational costs because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling, techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the past quarter, our focus has been on developing the theory and initial implementation behind high level models and search algorithms to accommodate these models.

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Document Details

Document Type
Technical Report
Publication Date
May 11, 1994
Accession Number
ADA280332

Entities

People

  • J. R. Rohlicek
  • Mari Ostendorf

Organizations

  • Boston University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Automated Speech Recognition
  • Bayesian Networks
  • Clustering
  • Covariance
  • Discrete Distribution
  • Electronic Mail
  • Hidden Markov Models
  • Language
  • Markov Models
  • Models
  • Natural Language Processing
  • Random Variables
  • Recognition
  • Signal Processing
  • Training
  • Vocabulary

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation
  • Speech Processing/Speech Recognition.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects