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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/9462


    Title: 語者辨識之研究;The study of speaker recognition
    Authors: 賴彥輔;Yen-Fu Lai
    Contributors: 電機工程研究所
    Keywords: 調適高斯混合模型;語者辨識;語者識別;語者驗證;adapted Gaussian mixture model;speaker verification;speaker recognition;speaker identification
    Date: 2003-06-05
    Issue Date: 2009-09-22 11:48:15 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 在本論文中,我們針對文字不特定的語者辨識系統,以高斯混合模型來代表每一位語者的聲紋特性。但是傳統的高斯混合模型需要大量的訓練語料,而且模型訓練時間長;為了改善這些缺點,我們利用語者調適的技術,將一個訓練良好的語者不特定模型調適成特定的語者模型。 我們使用訊號偏壓移除的技術來消除訓練語料中的通道效應,以獲得一個乾淨的語者不特定模型;此外,由於語者不特定模型的訓練語料龐大,為了減短訓練時間,我們採用向量量化的方法,事先將訓練語料作分群,再對每一群訓練一個高斯混合模型。 我們也將比較不同調適方法在語者辨識系統上的效果。在調適語料充足時,貝氏調適法可以有不錯的效果;但是在少量調適語料的情況下,模型中沒有調適的高斯分布會使得辨識的效能降低。因此對於少量的調適語料,我們提出一個加入模糊控制器的向量場平滑化演算法,以提升系統的辨識效能。 在本論文中,以100位語者來作語者辨識實驗。由實驗的結果可發現,本論文所使用之方法能夠在少量的語料下,快速的訓練出語者模型,並且也有良好的辨識效果。 In this thesis, we focus on the text-independent speaker recognition by using Gaussian mixture models (GMMs). However, general GMMs need large amounts of training data and training time; in order to improve these shortcomings, we use adapted GMM to replace the general GMMs. We get a clean speaker-independent model by using signal bias removal (SBR), and reduce the training time by vector quantization (VQ). Furthermore, we apply different adaptation methods to adapt the speaker models from a speaker-independent model. Maximum a posteriori (MAP) estimation has a good performance. However, on the condition of sparse adaptation data, some untrained parameters may reduce the performance. For this problem, we propose the approach of vector field smoothing by using a fuzzy controller to improve the performance.
    Appears in Collections:[電機工程研究所] 博碩士論文

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