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首页> 外文学位 >THE EXTRACTION OF FEATURES FROM A SPEECH SIGNAL CORRUPTED BY ADDITIVE NOISE AND THEIR USE FOR SPEECH ENHANCEMENT.
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THE EXTRACTION OF FEATURES FROM A SPEECH SIGNAL CORRUPTED BY ADDITIVE NOISE AND THEIR USE FOR SPEECH ENHANCEMENT.

机译：从具有附加噪声的语音信号中提取特征，并将其用于语音增强。

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摘要

The accurate extraction of two particular features from the speech signal affected by additive white noise is investigated. Reliable detection of the fundamental frequency of the vocal cord vibration provides important information on characteristics of the input excitation in the assumed digital model of the speech production. The determination of the fundamental (pitch) frequency is meaningful only for the voiced segments of speech; therefore the categorization of speech becomes another important issue. If the speech signal is affected by interfering noise, extraction of these features becomes a very difficult problem. The pitch tracking method consists of determining an integer frequency, between 70 and 300 Hz, which maximizes the weighted sum of the magnitudes of spectral lines in the Discrete Fourier Transformation of each analyzed frame of speech. Some additional conditions are added to compensate for the influence of the format structure on the harmonic sum function. The processing is dependent on the signal-to-noise ratio of the speech material. Extensive testing (for different signal-to-noise ratios) has shown that the pitch detector performs very well for both clean and noisy speech.;The voiced/unvoiced classifier uses a pattern recognition approach, more specifically--the nearest neighbor technique. The parameters used in V/UV decision are obtained as a byproduct of the pitch tracking procedure. The energy of the noisy signal is one parameter and the variance of the modified harmonic sum function is the second. The variance of this function has a remarkable property that is very resistant to the addition of noise to the speech signal. These parameters create a two-dimensional pattern space in which the two-class discrimination process takes place. The reference sets of measurements are obtained for different signal-to-noise ratios and are then used as the prototype sets in the classification process. The categorization technique was tested on non-noisy speech and on speech affected by additive noise. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of school.) UMI
机译：研究了从受加性白噪声影响的语音信号中准确提取两个特定特征的方法。可靠检测声带振动的基频可在语音产生的假定数字模型中提供有关输入激励特性的重要信息。基本（基音）频率的确定仅对语音的浊音部分有意义。因此，语音分类成为另一个重要问题。如果语音信号受到干扰噪声的影响，这些特征的提取将成为一个非常困难的问题。音调跟踪方法包括确定70到300 Hz之间的整数频率，该整数频率会在每个分析的语音帧的离散傅立叶变换中最大化频谱线幅度的加权和。添加了一些附加条件以补偿格式结构对谐波和函数的影响。该处理取决于语音材料的信噪比。广泛的测试（针对不同的信噪比）表明，音调检测器在干净语音和嘈杂语音方面表现都很好。有声/无声分类器使用模式识别方法，更具体地说-最近邻技术。 V / UV决策中使用的参数是音高跟踪过程的副产品。噪声信号的能量是一个参数，而修改后的谐波和函数的方差是第二个参数。该函数的方差具有非凡的性能，非常抗噪声添加到语音信号中。这些参数创建一个二维模式空间，在该模式空间中进行两级识别过程。针对不同的信噪比获得参考测量值集，然后将其用作分类过程中的原型集。在无噪语音和受加性噪声影响的语音上测试了分类技术。 。 。 。 （作者的摘要超出了规定的最大长度。经学校许可，在此停产。）UMI

著录项

  • 作者

    WALICKI, JACEK STANISLAW.;

  • 作者单位

    Marquette University.;

  • 授予单位 Marquette University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 1981
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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