Please use this identifier to cite or link to this item:
標題: 特定文字的語者驗證之研究
A Study of Text Dependent Speaker Verification
作者: 林裕昌
關鍵字: 動態時間校準(DTW);隱藏式馬可夫模式(HMM);線性預測參數;梅爾倒頻參數
出版社: 電機工程學系

Dynamic time warping (DTW) algorithm was widely used in speech recognition but it take large computation time and difficult to determine the thresholding value. Hidden Markov Models (HMM) provides a natural and highly reliable way of recognizing speech for a wide range of applications but it is too complex and too time consuming. In the thesis, we take some characteristics from DTW and HMM, and using the Gaussian distribution as front-end processes which can provide a good performance for voice verification.
In speaker verification, we take a voice from an unknown speaker to match a set of known speakers from database. Feature vectors are extracted from the voice samples by using Linear predictive coding (LPC) algorithm or Mel-Frequency Cepstral Coefficients (MFCC) algorithm. We compare the outcome of different feature vectors in both LPC and MFCC algorithm and to observe the influence of the state size. The simulation results show that using the LPC algorithm is better than using the MFCC algorithm in terms of the correctness to identify the right person with right password.
Appears in Collections:電機工程學系所

Show full item record
TAIR Related Article

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.