Summary and initial results of the 2013-2014 speaker recognition i-vector machine learning challenge

2014 ◽  
Author(s):  
Désiré Bansé ◽  
George R. Doddington ◽  
Daniel Garcia-Romero ◽  
John J. Godfrey ◽  
Craig S. Greenberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Speaker Recognition ◽  
Initial Results

Automatic Speaker Recognition System based on Optimised Machine Learning Algorithms

2019 ◽  
Author(s):  
Tumisho Billson Mokgonyane ◽  
Tshephisho Joseph Sefara ◽  
Thipe Isaiah Modipa ◽  
Madimetja Jonas Manamela
Keyword(s):  
Machine Learning ◽  
Speaker Recognition ◽  
Learning Algorithms ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Automatic Speaker Recognition

scholarly journals A Robust Isolated Automatic Speech Recognition System using Machine Learning Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 2325-2331
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Speaker Recognition ◽  
Search Algorithm ◽  
Recognition System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Speech Recognition System ◽  
Work Done

In order to make fast communication between human and machine, speech recognition system are used. Number of speech recognition systems have been developed by various researchers. For example speech recognition, speaker verification and speaker recognition. The basic stages of speech recognition system are pre-processing, feature extraction and feature selection and classification. Numerous works have been done for improvement of all these stages to get accurate and better results. In this paper the main focus is given to addition of machine learning in speech recognition system. This paper covers architecture of ASR that helps in getting idea about basic stages of speech recognition system. Then focus is given to the use of machine learning in ASR. The work done by various researchers using Support vector machine and artificial neural network is also covered in a section of the paper. Along with this review is presented on work done using SVM, ELM, ANN, Naive Bayes and kNN classifier. The simulation results show that the best accuracy is achieved using ELM classifier. The last section of paper covers the results obtained by using proposed approaches in which SVM, ANN with Cuckoo search algorithm and ANN with back propagation classifier is used. The focus is also on the improvement of pre-processing and feature extraction processes.

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