| 基于ICA与GA的语音特征提取方法 |
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| 引用本文： | 刘婷,史继飞.基于ICA与GA的语音特征提取方法[J].电脑与微电子技术,2013(21):24-28. |
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| 作者姓名： | 刘婷 史继飞 |
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| 作者单位： | [1]重庆邮电大学自动化学院,重庆400060 [2]重庆邮电大学计算机与技术学院,重庆400060 |
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| 摘 要： | 为了提高噪声环境中的语音识别率，将独立成分分析（ICA）方法用于语音信号特征提取．并使用遗传算法（GA）将提取出来的高维特征进行选择，最后得到的语音特征被用于基于高斯混合模型的语音识别应用中，并与传统的Mel倒谱系数（MFcC）特征进行比较。实验结果表明基于ICA与GA的语言特征优于传统的MFCC特征。
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| 关 键 词： | 独立成分分析 遗传算法 语音识别 噪声 |
Method of Speech Feature Extraction Based on Independent Component Analysis and Genetic Algorithm |
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| Authors: | LIU Ting SHI Ji-fei |
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| Affiliation: | 1. College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400060 ; 2. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400060) |
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| Abstract: | In order to improve the speech recognition in noisy environment, applies Independent Compo- nent Analysis （ICA） to obtain speech feature extraction. And uses Genetic Algorithm （GA） to select feature from the high-dimensional features. Uses the obtained feature in speech recogni- tion which is based on Gaussian Mixed Model （GMM）. Compared with normal Mel-Frequency Cepstral Cofficients（MFCC）. The experimental results show that the proposed ICA is better than normal MFCC. |
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| Keywords: | ICA GA Speech Recognition Noise |
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