EEG-Based Person Authentication with Variational Universal Background Model

This paper proposes a novel approach that combines the power of generative Gaussian mixture models (GMM) and discriminative support vector machines (SVM). The main objective this paper is to incorporating the GMM super vectors based on SVM classifier for language identification (LID) task. The GMM based LID system to capture all the variations present in phonotactic constraints imposed by the language requires large amount of training data. The Gaussian mixture model (GMM)- universal background model (UBM) modeling require less amount of training data. In GMM-UBM LID system, a language model is created by maximum a posterior (MAP) adaptation of the means of the universal background model (UBM). Here the GMM super vectors are created by concatenating the means of the adapted mixture components from UBM. Then these super vectors are applied to a SVM for classification purpose. In this paper, the performance of GMM-UBM LID system based on SVM is compared with the conventional GMM LID system. Form the performance analysis it is found that GMM-UBM LID system based on SVM is performed well when compared to GMM based LID system.

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Classification of ADHD and non-ADHD subjects using a universal background model

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2017.07.023 ◽

2018 ◽

Vol 39 ◽

pp. 204-212 ◽

Cited By ~ 7

Author(s):

Juan Lopez Marcano ◽

Martha Ann Bell ◽

A.A. (Louis) Beex

Keyword(s):

Background Model ◽

Universal Background Model

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A comparison of procedures for the calculation of forensic likelihood ratios from acoustic–phonetic data: Multivariate kernel density (MVKD) versus Gaussian mixture model–universal background model (GMM–UBM)

Speech Communication ◽

10.1016/j.specom.2010.09.005 ◽

2011 ◽

Vol 53 (2) ◽

pp. 242-256 ◽

Cited By ~ 37

Author(s):

Geoffrey Stewart Morrison

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Kernel Density ◽

Gaussian Mixture ◽

Background Model ◽

Likelihood Ratios ◽

Universal Background Model

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GMM-UBM Based Modeling for Language Identification using New Feature Vectors

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1919.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 3034-3039

Keyword(s):

Gaussian Mixture Model ◽

Mixture Model ◽

Gaussian Mixture ◽

Language Identification ◽

Training Data ◽

Background Model ◽

Indian Languages ◽

Feature Vectors ◽

Universal Background Model ◽

New Feature

The most of the existing LID systems based on the Gaussian Mixture model. The main requirement of the GMM based LID system is it require large amount of speech data to train the GMM model. Most of the Indian languages have the similarity because they are derived from Devanagari. Even though common phonemes exists in phoneme sets across the Indian languages, each language contain its unique phonotactic constraints imposed by the language. Any modeling technique capable of capturing all these slight variations imposed by the language is one of the important language identification cue. To model the GMM based LID system which captures above variations it require large number of mixture components.To model the large number of mixture components using Gaussian Mixture Model (GMM), the technique requires a large number of training data for each language class, which is very difficult to get for Indian languages. The main objective of GMM-UBM based LID system is it require less amount of training data to train(model) the system. In this paper, the importance of GMM-UBM modeling for language identification (LID) task for Indian languages are explored using new set of feature vectors. In GMM-UBM LID system based on the new feature vectors, the phonotactic variations imparted by different Indian languages are modeled using Gaussian Mixture model and Universal Background Model (GMM-UBM) technique. In this type of modeling, some amount of data from each class of language is pooled to create a universal background model. From this UBM model each model class is adapted. In this study, it is found that the performance of new feature vectors GMM-UBM based LID system is superior when compared to conventional new feature vectors based GMM LID system.

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