A Neural Network Based Speech Recognition System

Abstract

This report presents an overview of the development of a neural network based speech recognition system. The two primary tasks involved were the development of a time invariant speech encoder and a pattern recognizer or detector. The speech encoder uses amplitude normalization and a Fast Fourier Transform to eliminate amplitude and frequency shifts of acoustic clues. The detector consists of a back-propagation network which accepts data from the encoder and identifies individual words. This use of neural networks offers two advantages over conventional algorithmic detectors: the detection time is no more than a few network time constants, and its recognition speed is independent of the number of the words in the vocabulary. The completed system has functioned as expected with high tolerance to input variation and with error rates comparable to a commercial system when used in a noisy environment. Keywords: Artificial intelligence; Neural networks: Back propagation; Speech recognition.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1990
Accession Number
ADA219794

Entities

People

  • Edward J. Carrol
  • G. N. Reddy
  • Norman P. Coleman Jr.

Organizations

  • United States Army Armament Research, Development and Engineering Center

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Amplitude
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automated Speech Recognition
  • Computer Programming
  • Detection
  • Detectors
  • Engineering
  • Fast Fourier Transforms
  • Feature Extraction
  • Frequency
  • Frequency Shift
  • Identification
  • Neural Networks
  • Recognition
  • Speech Compression
  • Vocabulary

Readers

  • Computational Linguistics
  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks