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Related Concept Videos

Ellipses01:30

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An ellipse is formed when a right circular cone is intersected by an inclined plane that does not cut through its base. This intersection yields a closed, symmetric curve characterized by distinctive geometric properties. Most notably, an ellipse is defined as the collection of all points in a plane for which the combined distances to two fixed points—called the foci—remain constant.The ellipse features two principal axes: the major and the minor axes. The major axis is the longest diameter,...
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An ellipse is a fundamental conic section defined by the constant sum of distances from any point on its curve to two fixed points, known as the foci. This geometric property can be physically demonstrated using a pencil, string, and two pins. By anchoring the string at both ends and maintaining it taut with a pencil, one can trace the outline of an ellipse.The shape and extent of the ellipse are determined by its eccentricity, e, defined as the ratio of the distance between the center and a...
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: Jul 7, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification.

M W Mak1, S Y Kung

  • 1Center for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong.

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary
This summary is machine-generated.

Elliptical basis function (EBF) networks, incorporating full covariance matrices, improve speaker verification accuracy. These networks outperform traditional radial basis function (RBF) networks and vector quantization models, achieving a lower equal error rate.

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Last Updated: Jul 7, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Area of Science:

  • Speech processing
  • Machine learning
  • Pattern recognition

Background:

  • Radial basis function (RBF) networks are used for speaker verification.
  • Estimating basis function parameters is crucial for RBF network performance.

Purpose of the Study:

  • To introduce Elliptical Basis Function (EBF) networks by incorporating full covariance matrices into RBF networks.
  • To evaluate EBF networks using the Expectation-Maximization (EM) algorithm for parameter estimation in speaker verification tasks.

Main Methods:

  • Developing EBF networks with full covariance matrices.
  • Employing the Expectation-Maximization (EM) algorithm for parameter estimation.
  • Conducting text-independent speaker verification experiments using the TIMIT corpus with LP-derived cepstral coefficients.

Main Results:

  • Small EBF networks with EM-estimated parameters outperformed larger, conventionally trained RBF networks.
  • EBF networks achieved an equal error rate approximately two-thirds that of vector quantization (VQ)-based speaker models.
  • The proposed verification procedure demonstrated the applicability of EBF networks to speaker verification.

Conclusions:

  • EBF networks offer a more effective approach to speaker verification compared to traditional RBF networks and VQ models.
  • The EM algorithm is a suitable method for estimating parameters in EBF networks for improved performance.
  • The incorporation of full covariance matrices enhances the discriminative power of basis functions in speaker recognition.