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基于单高斯模型集的汉语美子带特征重建算法 被引量：2

Single Gauss Model Set Based MAP Data Imputation Method for Mel-Frequency Filter-Bank Vectors of Chinese Speech
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摘要 本文提出了基于单高斯模型集的汉语美子带特征重建 (SGMDI)方法 ,并通过试验研究了该算法对提高语音识别系统加性噪声鲁棒性的作用 .实验结果表明 :SGMDI方法能够明显提高语音识别系统对各类音子尤其是容易被加性噪声破坏的清辅音音子的识别正确率 ,从而显著增强了语音识别系统的噪声鲁棒性 . Single Gauss Model set based Data Imputation (SGMDI) method is developed to recover Mel-frequency-filter-bank vectors of Chinese speech. Experiments are carried out to study how SGMDI method improves Automatic Speech Recognition (ASR) system's robustness against additive noise. Experimental results show that SGMDI method can improve phoneme correction of all kind of phonemes. Especially for unvoiced phonemes, which are easily distorted by additive noise, phoneme correction will be significantly improved. Thus, ASR system's robustness against additive noise can be greatly improved by SGMDI method.
作者 罗宇 杜利民
机构地区 中科院声学所语音交互技术研究中心
出处 《电子学报》 EI CAS CSCD 北大核心 2004年第10期1654-1657,共4页 Acta Electronica Sinica
基金 国家 973重点基础研究发展项目"图像 语音 自然语言理解和知识挖掘 -汉语自然口语对话的理论和实验平台研究"(G 1 9980 30 50 5)
关键词 语音识别系统 子带 加性噪声 鲁棒性 重建算法 实验结果 正确率 单高斯模型集 汉语美子带特征重建 缺失特征方法 Algorithms Mathematical models Robustness (control systems) Spurious signal noise Vectors
分类号 TN912.34 [电子电信—通信与信息系统] TN919.3 [电子电信—通信与信息系统]
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参考文献8

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同被引文献16

  • 1王晶,傅丰林,张运伟.语音增强算法综述[J].声学与电子工程,2005(1):22-26. 被引量：22
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电子学报
2004年 第10期

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