Gaussian Mixture Models

  title={Gaussian Mixture Models},
  author={Douglas A. Reynolds},
  booktitle={Encyclopedia of Biometrics},
  • D. Reynolds
  • Published in Encyclopedia of Biometrics 2009
  • Computer Science
Definition A Gaussian Mixture Model (GMM) is a parametric probability density function represented as a weighted sum of Gaussian component densities. GMMs are commonly used as a parametric model of the probability distribution of continuous measurements or features in a biometric system, such as vocal-tract related spectral features in a speaker recognition system. GMM parameters are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori… 

Gaussian Mixture Model: A Modeling Technique for Speaker Recognition and its Component

This paper provides an overview of Gaussian Mixture Model (GMM) and its component of speech signal and shows how this model is appropriate for voice modeling in speaker recognition system.

Calculating Model Parameters Using Gaussian Mixture Models; Based on Vector Quantization in Speaker Identification

This paper has proposed a new approach for calculation of model parameters by using vector quantization (VQ) techniques to increase recognition accuracy and reduce the processing time of Gaussian Mixture Model and adapted Gaussian mixture model based speaker identification system.

Efficient speaker verification using Gaussian mixture model component clustering.

This paper proposes a method that utilizes clusters of GMM-UBM mixture component densities in order to reduce the computational load required and reports that Gaussian mixture reduction as proposed by Runnall's easily outperformed the other methods.

Gaussian Mixture Model and Gaussian Supervector for Image Classification

This paper borrows GSV from speech signal classification studies and applies it as an image representation for image classification, and proposes the Equal-Variance GMM (EV-GMM), where all the variables in all the Gaussian mixture components share the same variance.

Using the Gini Index for a Gaussian Mixture Model

This work proposes an efficient method to model a density function as a Gaussian mixture through an iterative algorithm that allow us to estimate the parameters of the model for a given data set through the Gini Index, a measure of the inequality degree between two probability distributions.

Generation of GMM Weights by Dirichlet Distribution and Model Selection Using Information Criterion for Malayalam Speech Recognition

In this work, the emission probabilities of syllables, based on HMMs are estimated from the Gaussian Mixture Model (GMM), and Mel Frequency Cepstral Coefficient (MFCC) technique is used for feature extraction from the input speech.

Comparative study of Gesture Recognition Using Gaussian Mixture Model, Support Vector Machine

Different types of gesture recognition using Gaussian mixture model has been described andvantages and comparison of GMM over other techniques like DTW,SVM are also discussed.

A Pseudometric for Gaussian Mixture Models

This paper proposes a similarity measure, Pseudometric for Gaussian Mixture Models (PmG), which is efficient in computation because of its closed-form expression for GMMs, and it ful⬁lls the triangle inequality which is necessary for many techniques like clustering and embedding.

Speaker recognition system based on pitch estimation

A new technique comparable to those existing using the frequency of vibration of the vocal cords called the fundamental frequency is designed, which is based on the YAAPT technique and modeled by a Gaussian mixture.



Robust text-independent speaker identification using Gaussian mixture speaker models

The individual Gaussian components of a GMM are shown to represent some general speaker-dependent spectral shapes that are effective for modeling speaker identity and is shown to outperform the other speaker modeling techniques on an identical 16 speaker telephone speech task.

Speaker Verification Using Adapted Gaussian Mixture Models

The major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs) are described.

Mixture Models

This paper discusses both issues and cover the basic methods of mixture models, and introduces some modern methods and gives numerous examples.

Vector quantization

  • R. Gray
  • Computer Science
    IEEE ASSP Magazine
  • 1984
During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes.

Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

Vibratory power unit for vibrating conveyers and screens comprising an asynchronous polyphase motor, at least one pair of associated unbalanced masses disposed on the shaft of said motor, with the

A Gaussian mixture modeling approach to text-independent speaker identification