Gaussian Mixture Models
VOICE RECOGNITION USING ARTIFICIAL NEURAL NETWORKS AND GAUSSIAN MIXTURE MODELS AARON NICHIE
... classification power [12]. The viability of machine learning approaches such as artificial neural networks (ANN) has also been explored as a useful technology to assist in statistical speech recognition [13-17] due to ...
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Speaker identification using distributed vector quantization and Gaussian mixture models
... a Gaussian mixture model for each enrolled ...of Gaussian mixture models whose parameters are determined using only those relevant acoustic features belonging to the ...based ...
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Gaussian mixture models and semantic gating improve reconstructions from human brain activity
... linear Gaussian framework for percept decoding with Gaussian mixture models to better represent the prior distribution of natural ...different mixture components correspond to different ...
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Semiparametric fractional imputation using Gaussian mixture models for handling multivariate missing data
... on Gaussian mixture models. Gaussian mixture model is a very flexible model that can be used to handle outliers, heterogeneity and skew- ...finite Gaussian mixture ...
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Enhanced Expectation–Maximization Clustering through Gaussian Mixture Models
... Abstract: Clustering is the most important task in data mining. For the intelligent clustering is also the part of the machine learning. Various existing systems are introduced for better clustering. In the past decade ...
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Microalgae classification using semi-supervised and active learning based on Gaussian mixture models
... Abstract Microalgae are unicellular organisms that have different shapes, sizes and structures. Classifying these microalgae manually can be an expensive task, because thousands of microalgae can be found in even a small ...
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Gaussian Mixture Models for Human Face Recognition under Illumination Variations
... a Gaussian Mixture Models (GMM)-based human face identification technique built in the Fourier or frequency domain that is robust to illumination changes and does not require “illu- mination ...
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Blob modification in counting vehicles using Gaussian mixture models under heavy traffic
... AI implementation on traffic light certainly requiring a process that is capable of recognizing and calculating traffic density as studied by Indrabayu et al. which uses Viola Jones method to detect and calculate the ...
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Analysis of a Modern Voice Morphing Approach using Gaussian Mixture Models for Laryngectomees
... This paper proposes a voicemorphing system for people suffering from Laryngectomy, which is the surgical removal of all or part of the larynx or the voice box, particularly performed in cases of laryngeal cancer. A ...
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EMOTION RECOGNITION FROM SPEECH WITH GAUSSIAN MIXTURE MODELS AND VIA BOOSTED GMM
... Speech has several endowment features such as naturalness and efficient, which makes it as winsome interface medium. It is possible to express emotions and attitudes via speech. In human machine interface application ...
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Serial and parallel implementations of model based clustering via parsimonious Gaussian mixture models
... of Gaussian mixture models, with parsimo- nious factor analysis-like covariance structure, is described and an efficient algorithm for its implementation is ...
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Speech Enhancement Using Gaussian Mixture Models, Explicit Bayesian Estimation and Wiener Filtering
... We emphasize that we used, in this paper, an explicit MMSE formula to estimate speech and noise PSDs, independently, which were then used in the construction of the Wiener filter. We also transformed this explicit MMSE ...
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Large-scale feature selection with Gaussian mixture models for the classification of high dimensional remote sensing images
... on Gaussian Mixture Models (GMM) have several interesting properties that make them suit- able for feature selection in the context of large amount of ...
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Hybrid generative-discriminative training of Gaussian mixture models
... discriminative models do not capture the input distribution of the data, their use in missing data scenarios is ...train Gaussian mixture models (GMMs) in a hybrid gen- erative-discriminative ...
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Training Gaussian Mixture Models at Scale via Coresets
... of Gaussian mixture models by exploiting a connection between statistical estimation and clustering problems in computational ...of mixture models for large data ...the models ...
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On Semi Supervised Learning of Gaussian Mixture Models for Phonetic Classification
... for Gaussian Mixture Models (GMM), resulting in a hybrid dis- criminative/generative objective ...Field models for text classification tasks, with the difference that the likelihood of labeled ...
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Text Independent Automatic Speaker Recognition System Using Mel Frequency Cepstrum Coefficient and Gaussian Mixture Models
... The aim of this paper is to show the accuracy and time results of a text independent automatic speaker recognition (ASR) system, based on Mel-Frequency Cepstrum Coefficients (MFCC) and Gaussian Mixture ...
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Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification using SIFT and RootSIFT Descriptors
... Abstract—Handwriting biometrics is the science of identifying the behavioural aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper ...
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Comparison of Approximation Methods to Kullback-Leibler Divergence between Gaussian Mixture Models for Satellite Image Retrieval
... parametric models in retrieval, the integral involved in com- puting the Kullback-Leibler divergence is not analytically tractable, which is the case for Gaussian mixture models ...
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A two-stage approach using Gaussian mixture models and higher-order statistics for a classification of normal and pathological voices
... The performance was assessed by averaging the results obtained from fivefold cross-validation scheme [10,12]. Table 1 shows the confusion matrix, accuracy (%) in- cluding 95% confidence intervals (CIs), specificity (%), ...
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