Formalizing Knowledge Used in Spectrogram Reading: Acoustic and Perceptual Evidence from Stops

Abstract

Since the invention of the sound spectrograph in 1946 by Koenig, Dunn and Lacey, spectrograms have been widely used for speech research. Over the last decade there has been revived interest in the application of spectrogram reading toward continuous speech recognition. Spectrogram reading involves interpreting the acoustic patterns in the image to determine the spoken utterance. One must selectively attend to many different acoustic cues, interpret their significance in light of other evidence, and make inferences based on information from multiple sources. While early attempts at spectrogram reading met with limited success (Klatt and Stevens, 1973; Lindblom and Svenssen, 1973; Svenssen, 1974), Zue, in a series of experiments intended to illustrate the richness of phonetic information in the speech signal (Cole et al., 1980; Cole and Zue, 1980), demonstrated that high performance phonetic labeling of a spectrogram could be obtained. In this thesis a formal evaluation of spectrogram reading was conducted in order to obtain a better understanding of the process and to evaluate the ability of spectrogram readers. The research consisted of three main parts: an evaluation of spectrogram readers on a constrained task, a comparison to listeners on the same task, and a formalization of spectrogram- reading knowledge in a rule-based system.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA206826

Entities

People

  • Lori F. Lamei

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Acoustic Measurement
  • Artificial Intelligence
  • Automata Theory
  • Automated Speech Recognition
  • Cognitive Science
  • Computational Science
  • Computer Languages
  • Computer Vision
  • Computers
  • Electrical Engineering
  • High Level Language Architecture
  • Language
  • Psychology
  • Reasoning
  • Signal Processing
  • Speech Analysis
  • Three Dimensional

Readers

  • Computational Linguistics
  • Speech Processing/Speech Recognition.
  • Theoretical Analysis.

Technology Areas

  • AI & ML