Human Speaker Recognition Based on the Integration of Genetic Algorithm and RBF Network

2010 ◽  
Author(s):  
Yan Zhou ◽  
Yunian Gu
Keyword(s):  
Genetic Algorithm ◽  
Speaker Recognition ◽  
Rbf Network

Speaker Recognition Study Based on Optimized Baum-Welch Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 2471-2473
Author(s):  
Hui Hong Xu ◽  
Su Chun Gao
Keyword(s):  
Genetic Algorithm ◽  
Speaker Recognition ◽  
Biological Recognition

The speaker recognition is a sort of biological recognition technology according to person's sound to identify .The article based on vc platform implement speaker recognitions function using VQ and HMM technology. using genetic algorithm to improve the Baum-Welch algorithm.Trough experiment verificate that improved-arithmetic enhance recognition effect.

scholarly journals Genetic Algorithm for Combined Speaker and Speech Recognition using Deep Neural Networks

2018 ◽  
Vol 2 ◽  
pp. 23-31 ◽  
Author(s):  
Gurpreet Kaur ◽  
Mohit Srivastava ◽  
Amod Kumar
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Speech Recognition ◽  
Speaker Recognition ◽  
Linear Prediction ◽  
Rate Sensitivity ◽  
Second Phase ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Perceptual Linear Prediction

Huge growth is observed in the speech and speaker recognition field due to many artificial intelligence algorithms being applied. Speech is used to convey messages via the language being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algorithm (GA) for combined speaker and speech recognition, relying on optimized Mel Frequency Cepstral Coefficient (MFCC) speech features, and classification is performed using a Deep Neural Network (DNN). In the first phase, feature extraction using MFCC is executed. Then, feature optimization is performed using GA. In the second phase training is conducted using DNN. Evaluation and validation of the proposed work model is done by setting a real environment, and efficiency is calculated on the basis of such parameters as accuracy, precision rate, recall rate, sensitivity, and specificity. Also, this paper presents an evaluation of such feature extraction methods as linear predictive coding coefficient (LPCC), perceptual linear prediction (PLP), mel frequency cepstral coefficients (MFCC) and relative spectra filtering (RASTA), with all of them used for combined speaker and speech recognition systems. A comparison of different methods based on existing techniques for both clean and noisy environments is made as well.

RBF Network Based on Improved Niche Hybrid Hierarchy Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 775-780
Author(s):  
Fang Wang ◽  
Jin Lan Yu ◽  
Pin Chang Zhu ◽  
Xi Feng Wei
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Chaotic Time Series ◽  
Premature Convergence ◽  
Rbf Network ◽  
Operation Speed ◽  
System Resource ◽  
Simulation Results

The improved niche hybrid hierarchy genetic algorithm is presented to overcome the premature convergence which happens in genetic algorithm constructing RBF network. The niche with poor fitness of every individual is eliminated to save system resource and raise operation speed. The simulation results demonstrate the better predicted performance on the Mackey-Glass chaotic time series than other algorithms.

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