基于神经网络的GMM优化语言检测

2010 Third International Joint Conference on Computational Science and Optimization Pub Date : 2010-05-28 DOI:10.1109/CSO.2010.21

A. Shadmand, K. Monfaredi

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引用次数: 1

摘要

语音信号的自动语言识别包括对不同语言进行建模和分类的算法和方法。高斯混合模型(Gaussian Mixture Model, GMM)是一种功能强大的特征向量分类工具。[1]中报道的波斯语验证系统使用GMM作为标记化的基本系统，并使用神经网络作为后端处理器。本文对“波斯语检测系统”进行了优化，使其应用于“语言验证系统”。

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Language Detection with GMM Optimization Using Neural Networks

The automatic language recognition of the speech signal consists of algorithms and methods which are used for modeling and classifying different languages. GMM (Gaussian Mixture Model), as a powerful instrument, can be used in classifying feature vectors. Persian language verification system, reported in [1], uses GMM as a basic system for tokenizing and Neural Network as the backend processor. In this paper a "Persian language detection system" is optimized to be used in "language verification system".

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2010 Third International Joint Conference on Computational Science and Optimization

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