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1)  Mel-frequency
Mel频率
1.
On the basis of analyzing the recognition of speech under G-Force by using each Mel-frequency scale,two kinds of modified Mel-frequency scales which can emphasize the effect of the middle frequency ranges are explored in this paper,consequently,the relative MFCCs are selected as the features for recognition of speech under G-Force.
文章在对应力影响下变异语音进行分频带分析的基础上,选用了可以提升语音信号中频段影响的修正Mel频率映射,并将其对应的MFCC系数作为新的语音识别特征。
2)  Mel-frequency cesptral coefficients
Mel频率倒谱
1.
The improved method based on fuzzy entropy,which analyzed and distilled the weight of speech duration,average magnitudes,pitch frequency,formants and Mel-frequency cesptral coefficients.
分析了发音持续时间、平均振幅、基音频率,第一共振峰和Mel频率倒谱参数,并基于模糊熵理论提取了各参数的权重。
3)  MFCC
Mel频率倒谱系数
1.
In this paper,we first propose an improved Mel-frequency cepstrum coefficients(PL-MFCC) which acquires by substituting logarithm by a new combined function fPL(x) to amend the noisy sensitivity of the logarithm.
通过研究在低能量段用幂函数代替自然对数函数对Mel滤波器组的输出进行处理,从而得到一种改进Mel频率倒谱系数(PL-MFCC)。
2.
In order to make identification from the speech signal,based on analysis of the conventional identical algorithm,it proposes an advanced method,which uses Mel Frequency Ceptral Coefficients(MFCC) as feature parameters.
为实现由语音信号进行说话人身份的辨识,研究了以往的实现说话人辨认的系统,提出一种改进的算法,采用能够反映人对语音感知特性的Mel频率倒谱系数(MFCC)作为特征参数,即基于概率神经网络(PNN)的识别方法。
3.
The MFCC feature of speech is extracted to recognize vowel(a,i,u)through SVM classifier.
Mel频率倒谱系数(MFCC)作为语音特征,通过SVM分类器进行元音a,i,u的识别,根据其对应量化后的语音能量,映射到嘴形序列,进行中值滤波和排除"奇异点"。
4)  Mel-Frequency Cepstral Coefficients(MFCC)
Mel频率倒谱参数
5)  MFCC
MEL频率倒谱系数(MFCC)
6)  Mel Frequency Principal Coefficient (MFPC)
Mel频率主分量参数(MFPC)
补充资料:频率计量(见时间频率计量)


频率计量(见时间频率计量)
frequency metrology: see time and frequency metrology

  口n IQ liliang顷率频率计皿(f比quency metrolo盯) 计t。见时闰
  
说明:补充资料仅用于学习参考,请勿用于其它任何用途。
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