Genetically Optimised Feedforward Neural Networks for Speaker Identification

Abstract

The problem of establishing the identity of a speaker from a given utterance has been conventionally addressed using techniques such as Gaussian Mixture Models (GMMs) that model the characteristics of a known speaker via means and covariances. In this paper we pose the task as a binary classification problem, and whilst in principle any one of a number of classifiers could be applied, this work compares the performance of genetically optimized neural networks versus the conventional approach of GMMs. The test data used in the experiments was the data used for the 1996 National Institute for Standards Technology (MST) evaluation of speaker identification systems.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1999
Accession Number
ADA367247

Entities

People

  • Jonathan Willmore
  • Richard Price
  • William Roberts

Organizations

  • Defence Science and Technology Group

Tags

Communities of Interest

  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Australia
  • Classification
  • Computers
  • Decision Support Systems
  • Detection
  • Engineering
  • False Alarms
  • Identification
  • Information Systems
  • Information Theory
  • Neural Networks
  • Recognition
  • Signal Processing
  • Standards
  • Surveillance
  • Systems Analysis
  • Universities

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Speech Processing/Speech Recognition.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Biotechnology