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.

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Document Details

Document Type
Technical Report
Publication Date
Oct 08, 1993
Accession Number
ADA271483

Entities

People

  • J. R. Rohlicek
  • Mari Ostendorf

Organizations

  • Boston University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Automated Speech Recognition
  • Classification
  • Contracts
  • Discrete Distribution
  • Electronic Mail
  • Language
  • Markov Models
  • Mathematical Analysis
  • Models
  • Natural Language Processing
  • Recognition
  • Resource Management
  • Signal Processing
  • Test Sets
  • Vocabulary
  • Word Recognition

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Operations Research
  • Speech Processing/Speech Recognition.

Technology Areas

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