Speech Recognition of Foreign Accent

Abstract

This thesis investigates the application of AutoRegressive (AR) modeling techniques on single syllable words to detect foreign accents in spoken American English. The study involves thirty-one native American English speakers, and six native Brazilian speakers. Five different distance measures are used for classification. Results show that correct classification is obtained for 88 % of the native English speakers and 80.5% of the non-native (foreign) English speakers. Speech processing, Foreign accent recognition, AutoRegressive (AR) modeling

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1994
Accession Number
ADA282979

Entities

People

  • John K. Dewey

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automated Speech Recognition
  • Classification
  • Cross Correlation
  • Electrical Engineering
  • Frequency Response
  • Identification
  • Language
  • Linguistics
  • Native Americans
  • New York
  • Performance Tests
  • Recognition
  • Signal Processing
  • Speech Analysis
  • United States
  • Word Lists

Readers

  • Speech Processing/Speech Recognition.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation