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).

scholarly journals Indonesian Stock Prediction using Support Vector Machine (SVM)

2018 ◽  
Vol 164 ◽  
pp. 01031 ◽  
Author(s):  
Murtiyanto Santoso ◽  
Raymond Sutjiadi ◽  
Resmana Lim
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Prediction Method ◽  
Gaussian Mixture ◽  
Machine Intelligence ◽  
Support Vector ◽  
Predicted Model ◽  
Python Programming ◽  
Model Support

This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence) or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK) stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data) and the remainder (20 %) was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

scholarly journals Video Summarization Based on Gaussian Mixture Model and Kernel Support Vector Machine for Forest Fire Detection

2019 ◽  
Vol 9 (1) ◽  
pp. 1827-1831
Keyword(s):  
Support Vector Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Forest Fire ◽  
Video Summarization ◽  
Fire Detection ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Kernel Support Vector Machine ◽  
Forest Fire Detection

Exponential growth in the generation of multimedia data especially videos resulted to the development of video summarization concept. The summary of the videos offers a collection of frames which precisely define the video content in a considerably compacted form. Video summarization models find its applicability in various domains especially surveillance. This paper intends to develop a video summarization technique for the application of forest fire detection. The proposed method involves a set of processes namely convert frames, key frame extraction, feature extraction and classification. Here, a Merged Gaussian Mixture Model (MGMM) is applied for the process of extracting key frames and kernel support vector machine (KSVM) is employed for classifying a frame into normal frame and forest fire frame. The simulation analysis is performed on the forest fire video files from FIRESENSE database and the results are assessed under several dimensions. The final outcome proves the efficiency of the presented MGMM-KSVM model in a considerable way.

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.

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