Microphone-Independent Robust Signal Processing Using Probabilistic Optimum Filtering

Abstract

A new mapping algorithm for speech recognition relates the features of simultaneous recordings of clean and noisy speech. The model is a piecewise nonlinear transformation applied to the noisy speech feature. The transformation is a set of multidimensional linear least-squares filters whose outputs are combined using a conditional Gaussian model. The algorithm was tested using SRI's DECIPHER(Trademark) speech recognition system. Experimental results show how the mapping is used to reduce recognition errors when the training and testing acoustic environments do not match.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1994
Accession Number
ADA460335

Entities

People

  • Leonardo Neumeyer
  • Mitchel Weintraub

Organizations

  • SRI International

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automated Speech Recognition
  • Bandwidth
  • Coefficients
  • Databases
  • Filtration
  • Hidden Markov Models
  • Markov Models
  • Microphones
  • Models
  • Probability
  • Recognition
  • Signal Processing
  • Standards
  • Test Sets
  • Word Recognition

Fields of Study

  • Engineering

Readers

  • Regression Analysis.
  • Speech Processing/Speech Recognition.

Technology Areas

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