| 病态嗓音特征的小波变换提取及识别研究 |
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| 引用本文： | 于燕平,胡维平. 病态嗓音特征的小波变换提取及识别研究[J]. 计算机工程与应用, 2009, 45(22): 194-196. DOI: 10.3778/j.issn.1002-8331.2009.22.062 |
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| 作者姓名： | 于燕平 胡维平 |
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| 作者单位： | 1. 广西师范大学物理与电子工程学院,广西,桂林,541004;柳州铁道职业技术学院电子工程系,广西,桂林,545007
2. 广西师范大学物理与电子工程学院,广西,桂林,541004 |
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| 摘 要： | 通过分析嗓音的发音机理、病态嗓音与正常嗓音在频域的表现差异,利用小波变换对信号进行分解,突出病态嗓音的特点,提出了基于多尺度分析的小波降噪、分解的熵系数(Entropy Coefficient based on De-noise,Decomposition of Multi-scale Analysis,ECDDMA)作为识别的特征矢量集。并对比分析了语音识别中经典特征参数Mel倒谱系数(MFCC),分别运用这两种特征参数对242例正常嗓音和234例病态嗓音运用高斯混合模型(GMM)进行了识别。结果显示:ECDDMA系数较传统的模拟人耳听觉非线性特性的MFCC及其动态特征能更准确地表征正常与病态嗓音之间的差异,有利于同时提高病态和正常嗓音的识别率。
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| 关 键 词： | 高斯混合模型(GMM) 病态嗓音 Mel倒谱系数(MFCC) 小波变换 |
| 收稿时间： | 2008-04-23 |
| 修稿时间： | 2008-7-24 |
Research of extracting of pathological voice's characteristics and recognition based on wavelet transformation and Gaussian mixture model |
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| YU Yan-ping,HU Wei-ping. Research of extracting of pathological voice's characteristics and recognition based on wavelet transformation and Gaussian mixture model[J]. Computer Engineering and Applications, 2009, 45(22): 194-196. DOI: 10.3778/j.issn.1002-8331.2009.22.062 |
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| Authors: | YU Yan-ping HU Wei-ping |
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| Affiliation: | YU Yan-ping1,2,HU Wei-ping11.College of Physics , Electronic Engineering,Guangxi Normal University,Guilin,Guangxi 541004,China 2.Department of Electronic Engineering,Liuzhou Railway Vocational Technical College,Liuzhou,Guangxi 545007,China |
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| Abstract: | Considering the voice pronunciation mechanism,the different performances of the abnormal voice and the normal voice in the field of frequency,the paper proposes a new method for extracting characteristics that is Entropy Coefficient based on De-noise,Decomposition of Multi-scale Analysis(ECDDMA) using the wavelet decomposition to find the pathological voice's characteristics,and comparative analysis of the effective speech characteristics MFCC.242 normal voices samples and 234 abnormal samples are recognize... |
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| Keywords: | Ganssian Mixture Model(GMM) pathological voice Mel Frequency Cepstrum Coefficient(MFCC) wavelet transformation |
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