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研究生:李信廷
研究生(外文):Shin-Ting Li
論文名稱:改善最小錯誤鑑別式之語者辨認方法
論文名稱(外文):Improved Minimum Classifiaction Error Method for Speaker Identification
指導教授:莊堯棠
學位類別:碩士
校院名稱:國立中央大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:55
中文關鍵詞:最小錯誤鑑別式語者辨認
外文關鍵詞:Speaker IdentificationMinimum Classifiaction Error
相關次數:
  • 被引用被引用:3
  • 點閱點閱:180
  • 評分評分:
  • 下載下載:13
  • 收藏至我的研究室書目清單書目收藏:0
在語者辨認中,能夠有效的訓練語料是非常重要的,因為這對辨識的效果是有很大的影響。到目前為止,傳統的語者模型都還是以最大相似度為準則,這在擁有大量訓練語料之下確實是有很好的效果,但在極少量訓練語料下卻不然,並且最大相似度估計的方法是,利用同一個語者的訓練語料去訓練出這個語者的模型,跟其它語者的訓練語料並無相關。,而此種模型訓練並沒有考慮到語者辨認時模型間彼此的關係,在模型參數訓練完成後有可能使得語音特徵向量落在對應的聲學模型與非相關模型的相似度值同時變大,產生辨識上的混淆。因此近十幾年來有所謂的鑑別式聲學模型訓練方法被提出來,不以最大化訓練聲學語料的相似度為目標,而以最小化分類(或辨識)錯誤為目標。
在本論文中,我們使用最小錯誤鑑別式法則重新去訓練語者模型,並提出了三個改善傳統最小錯誤鑑別式法則的方法。 此外,還把最小錯誤鑑別式使用在特徵語音調適法上,因為最小錯誤鑑別式受劣質近似模型的影響比最大相似度小。於是我們提出一個結合最小錯誤鑑別式和特徵語音調適法的方法,增加在極少語料時的強健性,以及降低建構聲學空間時造成劣質近似模型的影響性。
In the speaker identification, the data that can be effective training is very important, because this has very great influence on identification rate. Up to now, traditional speaker model use maximum likelihood. There is a very good result in a large amount of training data, but not good in a small amount of training data. The method of maximum likelihood is, use the training data for this speaker to train model for this speaker and not relevant with other speaker’s training data. This kind of training model which does not consider mutual relation among the models to verification.After the parameters are trained to finish,it may make the likelihood value of feature vectors leave the corresponding acoustics model and non- relevant model which become great at the same time,then produce the obscurity in verifying.So the so-called Discriminative Acoustic Model Training has been proposed in recent ten years.Do not regard maximizing to train acoustic data of likelihood as the goal, but regard minimizing classification(or identificaion) error as the goal.
In this thesis, we use minimum classification error to train speaker model again, and propose three method of improved traditional minimum classification error. In addition, also use minimum classification error in eigenvoices, because minimum classification error is smaller of mistake distinguishing than maximum likelihood. Then we purpose a method of to combine minimum classification error and eigenvoices, increase robust in a few data, and reduce influence of mistake distinguishing when construct acoustics space.
目錄
摘要 Ⅰ
目錄 Ⅱ
附圖目錄 Ⅴ
附表目錄 Ⅵ
第一章 緒論 1
1.1 研究動機 1
1.2語者辨識概述 2
1.3語者調適技術概述 4
1.4 研究方向 5
1.5 章節概要 7
第二章 語者識別之基本技術 8
2.1 特徵參數擷取 8
2.2語者模型建立 12
2.2.1高斯混合模型 13
2.2.2語者模型訓練流程 14
2.2.3向量量化 16
2.2.4 EM演算法 19
2.3語者模型調適技術 20
2.3.1貝式調適法 20
2.3.2特徵語音調適法 25
2.4語者識別 30
第三章 最小錯誤鑑別式 32
3.1 鑑別函式 33
3.2 綜合機率減少演算法 35
3.3 最小錯誤鑑別式之特徵語音調適 39
第四章 實驗結果 40
4.1 實驗環境 40
4.2 MCE實驗 42
4.2.1模型的遞迴次數之實驗 42
4.2.2門檻值對MCE之影響 43
4.2.3每次遞迴次數之競爭語者數目 45
4.2.4語料長度對MCE之影響 46
4.2.5改善的MCE與傳統的MCE比較 47
4.3 MCE結合Eigenvoices實驗 49
第五章 結論與未來展望 50
5.1 結論 50
5.2 未來展望 51
參考文獻 52
參考文獻
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