lpcc中文是什么意思

• 凝血酶原复合物

• "lpcd" 中文翻译 :    天冷就回来
• "lpc teko" 中文翻译 :    的打印机驱动器文件格式
• "lpcd45" 中文翻译 :    阳春白雪
• "lpc questionnaire" 中文翻译 :    最难共事者问卷
• "lpcf local procedure call facility" 中文翻译 :    本地过程调用设备
• "lpc low-order path connection" 中文翻译 :    低阶通道连接
• "lpci" 中文翻译 :    互链路协议控制信息
• "lpc longitudinal parity check" 中文翻译 :    纵向奇偶校验
• "lpci link protocol control information" 中文翻译 :    链路协议控制信息
• "lpc local procedure call" 中文翻译 :    本地规程调用

• The weighted lpcc feature based on the distor . .
  在后者中，采用了基于高. .
• The theory of lpc and mel - scaled cepstrum analysis is introduced in this dissertation and how to extract lpcc and mfcc is elaborated
  详细介绍了线性预测分析( lpc )和mel倒谱分析的原理及其具体实现过程。
• The paper discussed the bandpass filters analysis method and the technology of linear prediction code ， then reduced the lpcc and the mfcc parameters
  本文还介绍了语音信号分析方法中的滤波器组分析方法和线性预测编码技术，并推导了lpcc参数和mfcc参数。
• In this paper , we use full pole model to obtain speech signal lpc , then deduce it ' s lpcc , and we use the lpcc difference to describe speaker ' s track dynamic movement
  本文应用全极点模型，提取语音信号的线性预测系数，并推导出其倒谱系数，获得线性预测倒谱差分，用以描述说话人声道的动态变化。
• By analyzing speech characteristic parameters and the basis methods of speaker recognition , we choose mfcc and lpcc ' s difference to be the speech characteristic parameters . using dtw to recognize text - dependent speech , we have developed a speaker identification system in this paper
  本文通过分析语音特征参数的特点和说话人识别的基本方法，提出了以美尔倒谱差分和线性预测差分为特征，通过动态时间归整算法来识别的文本相关说话人辨认系统。
• In addition , the properties and extraction methods of some common feature parameters are studied in detail . in particular , two kinds of representative features , linear prediction cepstrum coefficient ( lpcc ) and mel - frequency cepstrum coefficient ( mfcc ) , are analyzed and researched on experimentation of vector quantization in text - independent speaker recognition . then , the performances of lpcc and mfcc are compared respectively on computer platform
  此外,我们在说话人识别试验中对常见的语音特征参数进行了研究,主要研究了线性预测倒谱( lpcc ) 、美尔倒谱( mfcc )等参数,并且分别基于计算机平台试验比较了lpcc和mfcc参数的识别效果。
• This thesis tries to update the cmdsr system to achieve the characters below : real - time , better robust , higher recognition rate , non - special - man . considering the disadvantages of traditional improved spectrum subtraction speech enhancement , this thesis proposes the theory of fuzzy spectrum subtraction based on the fuzzy theory and improved spectrum subtraction speech enhancement ; as for the difficulties of detecting the endpoint of speech signal , the thesis gives the table of initial and the improved parameters , with which we can confirm the endpoints of mandarin digit speech ; the thesis puts forward two - level digit real - time speech recognition system , the first level is based on discrete hidden markov model which is linear predictive coding cepstrum ( lpcc ) and difference linear predictive coding cepstrum ( dlpcc ) , the second level is based on formant parameters ; as for the realization of hardware , the thesis depicts the realization of every part of cmdsr based on the tms320vc5402 in detail ; as for the development of software , the thesis gives the software design flow chart of cmdsr , simulates the basic theory with matlab language and gives the simulation results
  针对传统的“改进谱相减法语音增强”参数设定单一、环境适应能力差的缺点,提出了一种利用模糊理论和“改进的谱相减法”结合的“模糊谱相减法语音增强” ;针对语音信号端点检测困难的特点,通过matlab仿真试验,给出了能够准确确定数码语音端点的初始和改进参数表;提出了利用基于线性预测编码倒谱参数和差分线性预测编码倒谱参数相结合的离散隐含马尔可夫模型进行第一级识别、利用共振峰参数进行第二级识别的两级汉语数码语音识别系统,在保证系统实时性的同时,实现连接汉语数码语音识别系统识别率的提高;在硬件实现上,详细阐述了基于tms320vc5402的连接汉语数码语音识别系统各部分硬件设计;在软件开发上,给出了连接汉语数码语音识别的软件设计各部分的流程图,并对各部分进行了matlab仿真,并给出了仿真结果。
• After analyzing the result of the experiment and the feature for text - independent recognition , we make a solution to the defects of the system . then long - time spectrum parameter and lpcc or mfcc are blended to be studied in detail to make the feature parameter . using these technologies , a high recognition rate was made
  针对单独使用lpcc或mfcc应用于说话人识别中存在识别率还不够高的问题,分析了原因,然后又对长时频谱特征进行了研究,并把长时频谱特征分别与lpcc和mfcc结合起来共同应用于说话人识别试验,从而在一定程度上减少了单独使用lpcc或mfcc运用于说话人识别中存在的问题,提高了系统的识别性能,取得了较好的效果。
• ( 2 ) two kinds of representative features , linear prediction cepstrum coefficient ( lpcc ) and mel - frequency cepstrum coefficient ( mfcc ) , are analyzed and researched . the performances of lpcc and mfcc are compared respectively on spce061a or computer platform . experiments indicate that the systems can gain better performances adopting lpcc than adopting mfcc in spce061a platform
  ( 2 )分析研究了线性预测倒谱( lpcc ) 、美尔倒谱( mfcc )等特征参数,并分别在计算机和spce061a平台比较了lpcc和mfcc参数的识别效果,实验表明在spce061a平台采用lpcc参数比采用mfcc参数有更好的识别效果。