【正文】 on, which has more contribution to the identification, by adjusting weighted coefficient. After adjustment, experiments show that system identification rate improved significantly. The recognition rate of the system pleted by this paper can reach 87% to the codebook database produced by 10 male voices. Key Words： speaker recognition。 對(duì)于 10 名男性語音的碼本庫， 本文實(shí)現(xiàn)系統(tǒng)的識(shí)別率可達(dá)到 87%。 本文 在 實(shí)現(xiàn) 系統(tǒng)的基礎(chǔ)上，分析了 組合特征中各分量對(duì)說話人識(shí)別的貢獻(xiàn)大小。 分 類決策方面， 本文使用矢量量化的方法完成對(duì)說話人語音信號(hào)的分類和判決。它屬于語音信號(hào)領(lǐng)域的一個(gè)模式識(shí)別問題。 說話人識(shí)別的目的是通過話語找出或核實(shí)說話人的身份 ，可以被用于訪問控制 。四種特征 加權(quán)擴(kuò)維得到的組合 特征矢量 最終表征了一個(gè)特定的 說話人。 SQL Server 2020 數(shù)據(jù)庫用于存儲(chǔ)已注冊(cè)說話人的語音碼本。實(shí)驗(yàn)表明，調(diào)整后系統(tǒng)識(shí)別率顯著提高。 speech signal. The system that is designed by this paper is achieved based on the Java language and SQL Server 2020 database. Java language is used to implement algorithms needed by speaker recognition, such as voice sampling, preprocessing, feature extraction, classification and decision and so on. SQL Server 2020 database is used to store registered speakers39。 vector quantification 北京 科技大學(xué)本科生畢業(yè)設(shè)計(jì)（論文） 1 目 錄 摘 要 ........................................................................................................................ 1 Abstract ........................................................................................................................... 1 引 言 ........................................................................................................................ 4 1 緒論 ............................................................................................................................ 5 本文利用到的聲學(xué)知識(shí) .................................................................................... 5 說話人識(shí)別的分類 ........................................................................................... 6 說話人確認(rèn)和說話人辨認(rèn) ...................................................................... 6 文本有關(guān)、文本無關(guān)和文本提示 ........................................................... 7 本文系統(tǒng)實(shí)現(xiàn)概述 ........................................................................................... 7 2 語音信號(hào)預(yù)處理 ......................................................................................................... 9 語音信號(hào)分幀 ................................................................................................... 9 語音信號(hào)端點(diǎn)檢測(cè) ......................................................................................... 10 本章小結(jié) ......................................................................................................... 13 3 說話人識(shí)別的特征提取 ............................................................................................ 14 特征參數(shù)的選取 ............................................................................................. 14 基音特征 ......................................................................................................... 15 自相關(guān)函數(shù) ........................................................................................... 15 基音檢測(cè) ............................................................................................... 16 線性預(yù)測(cè)倒譜系數(shù) ......................................................................................... 18 線性預(yù)測(cè)分析 ....................................................................................... 18 LPCC 求解 ............................................................................................. 20 差分特征 ......................................................................................................... 22 特征的組合 ..................................................................................................... 22 北京 科技大學(xué)本科生畢業(yè)設(shè)計(jì)（論文） 2 本章小結(jié) ......................................................................................................... 23 4 說話人識(shí)別的分類決策 ............................................................................................ 24 矢量量化 ......................................................................................................... 24 最佳碼本設(shè)計(jì) ................................................................................................. 26 LBG 算法 ............................................................................................... 27 初始碼本的生成 ................................................................................... 29 語音碼本的存儲(chǔ)和提取 .................................................................................. 29 語音碼本的存儲(chǔ) ................................................................................... 29 語音碼本的提取 ................................................................................... 30 測(cè)試語音的判決 ............................................................................................. 32 本章小結(jié) ......................................................................................................... 32 5 說話人識(shí)別的系統(tǒng)設(shè)計(jì) ............................................................................................ 33 語音信號(hào)采集的設(shè)計(jì) ...................................................................................... 33 語音信號(hào)預(yù)處理的設(shè)計(jì) .................................................................................. 34 語音信號(hào)特征提取的設(shè)計(jì) .............................................................................. 36 語音信號(hào)分類決策的設(shè)計(jì) .............................................................................. 37 本章小結(jié) ......................................................................................................... 39 6 本文設(shè)計(jì)系統(tǒng)的總結(jié)與分析 .................................................................................... 40 系統(tǒng)界面及使用效果 ...................................................................................... 40 兩個(gè)關(guān)鍵問題的論述 ...................................................................................... 43 預(yù)加重問題 ........................................................................................... 43 線性預(yù)測(cè)適 用性問題 ............................................................................ 44 系