跳到主要內容

臺灣博碩士論文加值系統

(54.91.62.236) 您好!臺灣時間:2022/01/17 23:41
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:鄭士賢
研究生(外文):Shi-Sian Cheng
論文名稱:高斯混合模型的學習與其在語者識別上的應用
論文名稱(外文):Model-based learning for Gaussian Mixture Model and its application on speaker identification
指導教授:傅心家傅心家引用關係
指導教授(外文):Hsin-Chia Fu
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊工程系
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:2002
畢業學年度:90
語文別:中文
論文頁數:51
中文關鍵詞:高斯混合模型語者識別貝氏資訊法則語者分段新聞主播
外文關鍵詞:gaussian mixture modelGMMBICclusteringspeaker identificationspeaker segmentationnews
相關次數:
  • 被引用被引用:2
  • 點閱點閱:332
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
本論文主要在探討高斯混合模型(Gaussian Mixture Model, GMM)的學習與其在語者識別(Speaker Identification)上的應用。在先前的研究中,用語者的GMM來做語者識別已經有很不錯的成果,但未對GMM的高斯元件個數與共變異數矩陣的型態(全(full)或對角(diagonal)共變異數矩陣)做深入的探討。在本論文中,我們提出一個“以BIC(Bayesian Information Criterion)為基礎的自我成長學習法”,用自動決定高斯元件的個數的方式來學習GMM;我們並且分別用全共變異數矩陣和對角共變異數矩陣的GMM來做語者識別,比較其實驗結果。我們將電視新聞節目錄成mpeg檔,從中擷取新聞主播的語料,其中包含了19位女主播和3位男主播。在此測試語料下,全共變異數矩陣GMM語者識別器的識別率可達95.84%;對角共變異數矩陣GMM語者識別器的識別率可達97.90%。我們並且用以GMM為基礎的語者識別方法來偵測新聞主播在新聞節目中的位置,做新聞故事的切割。我們用七小時的新聞節目作為測試資料,對於新聞主播的偵測我們有90.20%的精確率(precision rate),92.5%的召回率(recall rate)。
This paper mainly discusses the learning of Gaussian Mixture Model and its application on speaker identification. In the previous studies, it has been shown that using GMM for speaker
identification would perform well. But they do not discuss deeply about the number of gaussian component of GMM and the type of covariance matrix(full or diagonal). In this paper, we propose a BIC-based self-growing learning method for GMM and determine the number of gaussian component of each GMM automatically. We also use full covariance matrix GMM and diagonal covariance matrix GMM for speaker identification separately and then compare their experiment result. Our speaker database include 19 anchor woman and 3 anchor man from mpeg files that we captured from TV news by capture card. Under this database, the GMM speaker identifier with full covariance attains 95.84% identification accuracy rate, and 97.90% accuracy rate with diagonal covariance matrix. In this
paper, we also use the GMM-based speaker identification method for TV-news anchor detection and news story segmentation. We use 7 hours of TV-news program as testing data, and in our experiment the precision rate attains 90.20% and the recall rate attains 92.5%。
{第一章}緒論
{1.1}研究動機
{1.2}章節介紹
{第二章}以模型為基礎的高斯混合模型學習
{2.1}高斯混合模型和EM演算法
{2.2}K-means分群法和階層式凝聚分群法
{2.2.1}K-means分群法
{2.2.2}階層式凝聚分群法
{2.3}以模型為基礎的分群法
{2.3.1}模型選擇和貝氏資訊法則(Bayesian Information
Criterion)
{2.3.2}以模型為基礎的分群法
{2.4}以BIC為基礎的自我成長學習法於高斯混合模型
{2.4.1}演算法介紹
{2.4.2}實驗結果
{第三章}應用高斯混合模型於語者識別
{3.1}以高斯混合模型為基礎的語者識別
{3.2}實驗結果
{3.2.1}語者語料的收集
{3.2.2}實驗設計與結果
{第四章}應用高斯混合模型於新聞語料的分類與新聞故事的切割
{4.1}應用語者的高斯混合模型於新聞語料的分類
{4.2}電視新聞主播的偵測
{4.2.1}用BIC做語者交換點的偵測、分段與分群
{4.2.2}以語者識別為基礎的新聞主播偵測
{4.2.3}新聞主播偵測的實驗結果
{第五章}結論與未來方向
A. E. Raftery. { Bayesian Model Selection in Social Research.}
University of Washington Demography Center
Working paper no 94-12, September 1994.
Douglas A. Reynolds and Richard C. Rose.
{ Robust text-independent speaker
identification using Gaussian mixture speaker
models.}
IEEE transaction on speech and audio processing,
vol. 3, NO. 1, January 1995.
Douglas A. Reynolds.
{ Large population speaker identification using
clean and telephone speech. }
IEEE Signal Processing Letters, Volume: 2
Issue: 3, March 1995 Page(s): 46-48
Lan Wang, Ke Chen, and Huisheng Chi.
{ Capture interspeaker information with a neural
network for speaker identification.}
Neural Networks, IEEE Transactions on, Volume:
13 Issue: 2, March 2002 Page(s): 436 -445
S. Chen, P.Gopalakrishnan.
{ Speaker, environment and channel change
detection and clustering via Bayesian
Information Criterion.}
Proc. Broadcast News Trans. Under. Workshop,
pp.127-132, Feb., 1998.
Bowen Zhou and J. L. Hansen.
{ Unsupervised audio stream segmentation and
clustering via the Bayesian Information
Criterion.}
International Conference on Spoken Language
Processing, pp. 714-717, Beijing, China, Oct.
2000.
G. Schwarz.
{ Estimating the dimension of a model.}
The annals of Statistics, vol.6, pp.461-
464,1978.
J.Q. Li and A. R. Barron.
{ Mixture density estimation.}
In Advances in Neural Information Processing
Systems 12. He MIT Press, 2000.
Figueiredo, M.A.F.; Jain, A.K.
{ Unsupervised learning of finite mixture
models.}
Pattern Analysis and Machine Intelligence,
IEEE Transactions on, Volume: 24 Issue: 3,
March 2002. Page(s): 381 -396.
Feng, Z. D., and McCulloch, C. E.
{ Using bootstrap likelihood ratios in
finite mixture models.}
J. R. Statist. Soc. B, 58(3),609-617, 1966.
Ripley, B. D. (1996).
{ Pattern Recognition and neural networks.}
Cambridge University Press.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊