diagnosis of corneal diseases is possible by measuring and evaluation of corneal thickness in different layers. thus, the need for precise segmentation of corneal layer boundaries is inevitable. obviously, manual segmentation is time‑consuming and imprecise. in this paper, the gaussian mixture model (gmm) is used for automatic segmentation of three clinically important corneal boundaries on opt...

automatic segmentation of multiple sclerosis (ms) lesions in brain magnetic resonance imaging (mri) has been widely investigated in the recent years with the goal of helping ms diagnosis and patient follow‑up. in this research work, gaussian mixture model (gmm) has been used to segment the ms lesions in mris, including t1‑weighted (t1‑w), t2‑w, and t2‑fluid attenuation inversion recovery. usual...

In this report, we provide a theoretical discussion on temporal data cluster analysis: does the data come from one source or two sources; is it better to cluster the data into two clusters or leave it as one cluster. Here we analyse only the simplest case: when the data comes from two symmetric Gaussian probability-densityfunctions (pdfs), i.e., with same variance and same absolute value of the...

Personal identity identification is an important requirement for controlling access to protected resources. Biometric identification by using certain features of a person is a more secured solution for security identification. Advances in speech processing technology and digital signal processors have made possible the design of high-performance and practical speaker recognition systems. A more...

Particle filtering (PF) has been widely used in solving nonlinear/non Gaussian filtering problems. Inferring to the target tracking in a wireless sensor network (WSN), distributed PF (DPF) was used due to the limitation of nodes’ computing capacity. In this paper, a novel filtering method—asynchronous DPF (ADPF) for target tracking in WSN is proposed. There are two keys in the proposed algorith...

Gaussian mixture model (GMM) is generally used to estimate the speaker model from speech for speaker identification. In this paper, we propose the method that estimates the optimal number of Gaussian mixtures based on incremental k-means for speaker identification. In the proposed method, the initialization with the optimal number of mixtures is done by adding dynamically the number of mixtures...

Gaussian Mixture Models (GMMs) has been proposed for off-line signature verification. The individual Gaussian components are shown to represent some global features such as skewness, kurtosis, etc. that characterize various aspects of a signature, and are effective for modeling its specificity. The learning phase involves the use of Gaussian Mixture Model (GMM) technique to build a reference mo...

In this work, we conducted an empirical comparative study of the performance text-independent speaker verification in emotional and stressful environments. This work combined deep models with shallow architecture, which resulted novel hybrid classifiers. Four distinct were utilized: neural network-hidden Markov model (DNN-HMM), network-Gaussian mixture (DNN-GMM), Gaussian model-deep network (GM...

An automatic Language Identification (LID) is the task of automatically recognizing a language from the given spoken utterance. Language identification is used to identify the language of the particular audio and reduce the complexity of the audio sample. LID systems that rely on multiple language phone recognition language modeling (PRLM) and n-gram language modeling produces the best performa...

This paper provides an overview of Gaussian Mixture Model (GMM) and its component of speech signal. During the earlier period it has been revealed that Gaussian Mixture Model is very much appropriate for voice modeling in speaker recognition system. For Speaker recognition, Gaussian mixture model is an essential appliance of statistical clustering. The task effortlessly performed by humans is n...