TY - EJOUR
PY - 2024
DA - February
TI - GMM Optimization Using Neural Networks for Persian Language Detection
T2 - Majlesi Journal of Electrical Engineering
VL - 2
L1 - https://oiccpress.com/Majlesi-Journal-of-Electrical-Engineering/article/gmm-optimization-using-neural-networks-for-persian-language-detection/
DO - 10.1234/mjee.v2i2.59
N2 - Language identification (LID) in speech signals is an important classification task. In this paper Persian language verification is proposed and evaluated. The system is developed by using Gaussian mixture models as a basic system for tokenizing and a Neural Network as the backend processor. Gaussian Mixture Models can be utilized to model the distribution of feature vector in speech signals for classification. We gathered our language identification corpus from different Satellite TV channels. The results are presented for a system using the GMM Tokenizer in combining with Neural Network. The results of GMM-NN system compared with GMM-Tokenizer system. It is shown that using the Neural Network as the backend processor improves the results significantly.
IS - 2
PB - OICC Press
KW - Tokenizer. Language Verification, Neural network, en, SDC, LID, GMM 
EN -