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

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.

scholarly journals Language Identification based on Support Vector Machine using GMM Super vectors

2020 ◽  
Vol 9 (4) ◽  
pp. 1983-1986
Keyword(s):  
Language Model ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Language Identification ◽  
Training Data ◽  
Background Model ◽  
Support Vector ◽  
Svm Classifier ◽  
Map Adaptation ◽  
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.

scholarly journals A Review on Text-Independent Speaker Verification Techniques in Realistic World

2016 ◽  
Vol 9 (1) ◽  
pp. 36-40
Author(s):  
Renu Singh ◽  
Arvind Singh ◽  
Utpal Bhattacharjee
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Speaker Verification ◽  
Gaussian Mixture ◽  
Background Model ◽  
Support Vector ◽  
Universal Background Model ◽  
Verification Techniques ◽  
Text Independent Speaker Verification

This paper presents a reviewof various speaker verification approaches in realistic world, and explore a combinational approach between Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) as well as Gaussian Mixture Model (GMM) and Universal Background Model (UBM).

Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

2015 ◽  
Vol 734 ◽  
pp. 463-467 ◽  
Author(s):  
Pan Pan Zhang ◽  
Chun Yang Mu ◽  
Xing Ma ◽  
Fu Lu Xu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Background Model ◽  
Morphological Filter ◽  
Improved Method ◽  
Moving Vehicle ◽  
Model Based ◽  
Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

scholarly journals POLARIMETRIC SAR DATA GMM CLASSIFICATION BASED ON IMPROVED FREEMAN INCOHERENT DECOMPOSITION

2016 ◽  
Vol XLI-B7 ◽  
pp. 341-345
Author(s):  
S. Rouabah ◽  
M. Ouarzeddine ◽  
B. Azmedroub
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Urban Areas ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
Polarimetric Sar ◽  
Data Set ◽  
Feature Vectors ◽  
Sar Data

Due to the increasing volume of available SAR Data, powerful classification processings are needed to interpret the images. GMM (Gaussian Mixture Model) is widely used to model distributions. In most applications, GMM algorithm is directly applied on raw SAR data, its disadvantage is that forest and urban areas are classified with the same label and gives problems in interpretation. In this paper, a combination between the improved Freeman decomposition and GMM classification is proposed. The improved Freeman decomposition powers are used as feature vectors for GMM classification. The E-SAR polarimetric image acquired over Oberpfaffenhofen in Germany is used as data set. The result shows that the proposed combination can solve the standard GMM classification problem.

Ensemble Gaussian mixture model-based special voice command cognitive computing intelligent system

2020 ◽  
Vol 39 (6) ◽  
pp. 8181-8189
Author(s):  
P. Saravanan ◽  
E. Sri Ram ◽  
Saikishor Jangiti ◽  
E. Ponmani ◽  
Logesh Ravi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Intelligent System ◽  
Gaussian Mixture ◽  
Training Data ◽  
Neurological Injury ◽  
Voice Command ◽  
Model Based ◽  
Dysarthric Speech ◽  
Impaired Speech

Dysarthria is a speech disorder caused by stroke, Parkinson’s disease, neurological injury, or tumors that damage the nervous system and weaken the speech quality. Developing a unique voice command system for Dysarthric speech helps to recognize impaired speech and convert them into text or input commands. Hidden Markov Model (HMM) is one of the widely used generative model-based classifiers for Dysarthric speech recognition. But due to insufficient training data, HMM doesn’t provide optimal results on overlapping classes. We propose an ensemble Gaussian mixture model to recognize impaired speech more accurately. Our model converts the sequence of feature vectors into a fixed dimensional representation of patterns with varying lengths. The performance efficiency of the proposed model is evaluated on the Dysarthric UA-speech benchmark dataset. The discriminatory information provided by the proposed approach yields better classification accuracy even for shallow intelligibility words compared to conventional HMM.

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