lpcc 中文意思是什麼

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

針對傳統的「改進譜相減法語音增強」參數設定單一、環境適應能力差的缺點，提出了一種利用模糊理論和「改進的譜相減法」結合的「模糊譜相減法語音增強」 ;針對語音信號端點檢測困難的特點，通過matlab模擬試驗，給出了能夠準確確定數碼語音端點的初始和改進參數表;提出了利用基於線性預測編碼倒譜參數和差分線性預測編碼倒譜參數相結合的離散隱含馬爾可夫模型進行第一級識別、利用共振峰參數進行第二級識別的兩級漢語數碼語音識別系統，在保證系統實時性的同時，實現連接漢語數碼語音識別系統識別率的提高;在硬體實現上，詳細闡述了基於tms320vc5402的連接漢語數碼語音識別系統各部分硬體設計;在軟體開發上，給出了連接漢語數碼語音識別的軟體設計各部分的流程圖，並對各部分進行了matlab模擬，並給出了模擬結果。

Considering system security, we adopt mfcc to recognize password and lpcc to represent speaker track dynamic movement. the double decrees enable it applying in high secret situations. the system has many merit such as the quick operation velocity, easy model update, less calculate quantity and low error rate

本文考慮到系統的安全性，採用美爾倒譜系數識別密碼，線性預測倒譜差分識別說話人聲道動態變化的雙重判決方法，為系統應用在高度機密場合提供了可能，具有運算速度快，模板更新容易，計算量小，差錯率低等優點。

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

本文應用全極點模型，提取語音信號的線性預測系數，並推導出其倒譜系數，獲得線性預測倒譜差分，用以描述說話人聲道的動態變化。

In the phase of training, it gets the sampling data from the wave files which were stored in the voice library by using the mci functions. then calculates the character vector ( 12 ranks of lpc and lpcc ) and trains them by clustering method, so we get the templates used by speech - recognition, this templates were stored in the template library. in the state of recognition, after calculating the character vector of input voice, we compare it with the character vectors of templates, and then find the best one or refuse it

系統的組成模塊與語音識別系統的基本構成模型基本一致，在訓練過程中，通過調用mci （ mcimultimediacontrolinterface ）提供的函數從語音庫中的波形文件中讀取采樣數據，分幀計算出由12維線性預測系數和12維線性預測倒譜系數構成的特徵矢量，並按照聚類的方法進行訓練，得到后續語音識別時需要的模板，存放于模板庫中。

The paper involves two parts of closed - set text - independent speaker identification systems in noise. firstly, the paper introduces shortcomings of the parameters, such as lpcc, mfcc etc. so sub - band energy cepstral feature parameters based on multi - rate sub - band processing and teager energy operator are described. while the paper discusses the use of rayleigh cepstral liftering to weigh sub - band cepstral coefficients so as to emphasize speaker personality information

首先本文介紹了各種特徵提取的方法，如lpcc 、 mfcc等，在分析了它們的缺點的基礎上，本文根據人耳頻率子帶獨立識別的特性和能量運算元，提出了子帶能量倒譜特徵參數，並用提出的瑞利倒譜提升進行加權，突出說話人的個性信息。

The theory of lpc and mel - scaled cepstrum analysis is introduced in this dissertation and how to extract lpcc and mfcc is elaborated

詳細介紹了線性預測分析（ lpc ）和mel倒譜分析的原理及其具體實現過程。

The paper discussed the bandpass filters analysis method and the technology of linear prediction code ， then reduced the lpcc and the mfcc parameters

本文還介紹了語音信號分析方法中的濾波器組分析方法和線性預測編碼技術，並推導了lpcc參數和mfcc參數。

According to commonly steps of speech recognition, the key methods of speech recognition is discussed : a ) firstly, preprocessing and feature extraction in speech recognition is studied. we studied two important speech analysis methods and extracted two key features for speech recognition : mfcc and lpcc. on the base of the research we improve the algorithm and experiment with new method

論文根據語音識別的一般流程，主要針對語音識別系統的關鍵技術進行探討： （ 1 ）首先對語音信號的預處理和特徵提取問題進行討論。分析了當前最常用的兩種特徵參數， mfcc和lpcc ，在此基礎上對語音識別系統預處理和特徵提取作了一些改進，並給出相應的實驗驗證。

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

針對單獨使用lpcc或mfcc應用於說話人識別中存在識別率還不夠高的問題，分析了原因，然後又對長時頻譜特徵進行了研究，並把長時頻譜特徵分別與lpcc和mfcc結合起來共同應用於說話人識別試驗，從而在一定程度上減少了單獨使用lpcc或mfcc運用於說話人識別中存在的問題，提高了系統的識別性能，取得了較好的效果。