博碩士論文 89521073 詳細資訊


姓名 吳金池(Chin-Chih Wu)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 語者辨識系統之研究
(The Research of Speaker Recognition System)
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摘要(中) 摘 要
近些年來,由於語音辨識技術的純熟,語者辨識的研究已經越來越受到重視,且廣泛的應用在各種領域中,而本實驗室對於國語語音辨識的研究已經累積了不少成果,但對於語者辨識這個領域較少涉獵。因此,本論文是針對國語語音資料,建立語者身分識別系統,來對語者辨識做一個初步研究。
在本論文中,以高斯混合模型來代表每一位語者特徵向量的統計分佈,高斯混合模型是語者辨認中最常使用的統計模型,實用上共變異矩陣常假設為對角化型式,這個假設忽略了特徵參數間的相關性,但若使用全共變異矩陣,則計算量及參數量將大大的提昇,而顯的不切實際。因此本篇論文主要是針對幾種不同特徵參數的轉換,將全共變異矩陣轉換為對角共變異矩陣,使系統有最佳效能,並結合向量量化的方法訓練模型,運用於語者識別上。
而實驗中以100位語者,來做語者辨識之實驗,從實驗中可發現經過特徵參數轉換後所求得的對角共變異矩陣,比未做任何處理的高斯混合模型來的好。
摘要(英) Abstract
In recent years, the characteristics in human biology, such as the fingerprint, palm prints, eye and voice etc, were used to recognize personal identities. Among these biological characteristics, the human speech has the properties as easiness of product and extraction, as well as transmission through the network of telephone, and therefore suits for application of human identity.
The Gaussian mixture model (GMM) is the most using statistical model for speaker recognition. In this plan, use the Gauss mixture model to represent each speaker distribute of feature vector. The covariance matrices can be full or diagonal matrices forms in GMM. Most of these models assume diagonal covariance matrices, but this assumes to ignore the each feature vectors correlation. If we assume the covariance matrix is diagonal form, we must use the orthonormal transformation to derive uncorrelated feature vectors. So that to reduce the relation of each vector of feature to lowest. This technique can improve the performance of speaker or speech recognition system.
關鍵字(中) ★ 語者確認
★ 語者辨識
★ 語者識別
關鍵字(英) ★ speaker identification
★ speaker recognition
★ speaker verification
論文目次 目錄
摘要...........................Ⅰ
致謝........................... Ⅱ
目錄........................... Ⅲ
附圖目錄.........................Ⅵ
附表目錄.........................Ⅷ
第一章 緒論.......................1
1.1 研究動機.................... 1
1.2 語者辨識系統概述................ 2
1.3 適用於語者辨識的模型.............. 4
1.4 語音資料庫................... 4
1.5 章節概要.................... 5
第二章 語音處理與語者辨識基本技術............6
2.1 特徵參數擷取.................. 6
2.2 語者模型................... . 9
2.2.1 高斯混合語者模型.............. 9
2.2.2 使用高斯混合模型的理由........... 11
2.2.3 模型訓練與參數預估............. 12
2.3 語者辨識方法..................17
2.3.1 語者識別................. 17
2.3.2 語者確認................. 17
2.3.3 門檻值選取................18
第三章 系統架構.....................19
3.1 特徵參數之正交轉換...............20
3.1.1 正交轉換之高斯混合模型延伸........ 22
3.1.2 分散式之正交轉換.............. 23
3.1.3 埋入式高斯混合模型............ 24
3.2 向量量化高斯混合模型..............27
3.2.1結合向量量化與特徵參數正交轉換...... 29
3.3 雙重相似度計分方法...............32
3.4 語者確認方法..................33
3.4.1.1反語者模型(Anti-Speaker Model)....... 33
3.4.1.2全域者模型(Global Speaker Model)...... 35
第四章 語者辨識實驗.................. 37
4.1 高斯混合模型系統設定實驗............ 37
4.1.1 特徵參數階數的影響............ 37
4.1.2 混合數的影響............... 38
4.1.3 訓練語料長度的影響............. 41
4.2 語者識別實驗..................42
4.2.1 共變異矩陣形式的影響............ 42
4.2.2 不同特徵參數轉換方式的比較......... 44
4.2.3 傳統GMM與VQGMM的比較........ 46
4.2.4 VQOGMM與其它轉換方式之比較結果..... 47
4.2.5 雙重相似度計分方法的實驗結果....... 49
4.3 語者確認實驗..................50
4.3.1非正規化與正規化之語者確認實驗結果..... 50
4.3.2 背景語者人數對系統的影響......... 52
4.3.3 不同背景語者模型及測試語料長度對系統的影響53
第五章 結論與未來展望.................56
參考文獻.........................58
參考文獻 參考文獻
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[20] 陳明熒, “PC電腦語音辨認實作”, 旗標出版有限公司,中華民國83年1月。
指導教授 莊堯棠(Yau-Tarng Juang) 審核日期 2002-6-5
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