Robust impaired speech segmentation using neural network mixture model

2014 ◽  
Author(s):  
Sunday Iliya ◽  
Dylan Menzies ◽  
Ferrante Neri ◽  
Pip Cornelius ◽  
Lorenzo Picinali
Keyword(s):  
Neural Network ◽  
Mixture Model ◽  
Speech Segmentation ◽  
Impaired Speech

scholarly journals A Hybrid Hidden Markov Model for Pipeline Leakage Detection

2021 ◽  
Vol 11 (7) ◽  
pp. 3138
Author(s):  
Mingchi Zhang ◽  
Xuemin Chen ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
State Sequence ◽  
Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

Noise Classification Based on GMM and AANN

2011 ◽  
Vol 58-60 ◽  
pp. 1847-1853 ◽  
Author(s):  
Yan Zhang ◽  
Cun Bao Chen ◽  
Li Zhao
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Specific Method ◽  
Accuracy Rate ◽  
Noise Classification ◽  
Distribution Type

In this paper, Gaussian Mixture model (GMM) as specific method is applied to noise classification. On this basis, a modified Gaussian Mixture Model with an embedded Auto-Associate Neural Network (AANN) is proposed. It integrates the merits of GMM and AANN. We train GMM and AANN as a whole and they are trained by means of Maximum Likelihood (ML). In the process of training, the parameter of GMM and AANN are updated alternately. AANN reshapes the distribution of the data and improves the similarity of the feature data in the same distribution type of noise. Experiments show that the GMM with embedded AANN improves accuracy rate of noise classification against baseline GMM.

Mixture Models Based on Neural Network Averaging

2006 ◽  
Vol 18 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Walter W. Focke
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Mixture Model ◽  
Network Architecture ◽  
Binary Data ◽  
Data Sets ◽  
Wilson Equation ◽  
Power Mean ◽  
Hidden Layer ◽  
Box Cox Transformation

A modified version of the single hidden-layer perceptron architecture is proposed for modeling mixtures. A particular flexible mixture model is obtained by implementing the Box-Cox transformation as transfer function. In this case, the network response can be expressed in closed form as a weighted power mean. The quadratic Scheffé K-polynomial and the exponential Wilson equation turn out to be special forms of this general mixture model. Advantages of the proposed network architecture are that binary data sets suffice for “training” and that it is readily extended to incorporate additional mixture components while retaining all previously determined weights.

scholarly journals AN ENHANCED METHOD OF LIVER LESION DETECTION USING DEEP NEURAL NETWORK, WATERSHED TRANSFORM AND GAUSSIAN MIXTURE MODEL TECHNIQUES IN MR IMAGES

2021 ◽  
pp. 335-340
Author(s):  
A.BathshebaParimala ◽  
R.S.Shanmugasundaram
Keyword(s):  
Neural Network ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Deep Neural Network ◽  
Gaussian Mixture ◽  
Metastatic Carcinoma ◽  
Neural Network Classifier ◽  
Mr Images ◽  
Malignant Growth ◽  
Aided Diagnosis

Cancer of the liver is one of the leading causes of death all over the world. Physically recognising the malignancy tissue is a difficult and time-consuming task. In the future, a computer-aided diagnosis (CAD) will be used in dynamic movement to determine the precise position for care. As a result, the primary goal of this research is to use a robotized approach to precisely identify liver cancer. Methods: In this paper, we suggest a new approach called the watershed Gaussian based deep learning (WGDL) strategy for accurately portraying malignant growth sores in liver MRI images. This project used a total of 150 images to build the proposed model. The liver was first isolated using a marker-controlled watershed division scale, and the malignancy-induced injury was then divided using the Gaussian mixture model (GMM) algorithm. Different surface highlights were removed from the sectioned locale after tumour division. These jumbled highlights were fed into a deep neural network (DNN) classifier for a computerised classification of three types of liver cancer: haemangioma (HEM), hepatocellular carcinoma (HCC), and metastatic carcinoma (MET). The following are the outcomes: Using a Deep Neural Network classifier and an unimportant approval deficiency of 0.053 during the characterization period, we were able to achieve a grouping precision of 98.38 percent at 150 ages. The system in our proposed approach is suitable for testing with a large data set and can assist radiologists in detecting liver malignant growth using MR images. KEYWORDS: computer-aided diagnosis (CAD), watershed Gaussian based deep learning, Gaussian mixture model, hepatocellular carcinoma, metastatic carcinoma, Deep Neural Network classifier

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