| Summary: | 碩士 === 國立中興大學 === 通訊工程研究所 === 99 === A mixed-type feature extraction algorithm used three kinds of feature extraction method, respectively there are LPC, MFCC and GTCC. The use of LPC can remove formant and estimate amplitude and frequency of speech signal. The use of MFCC can transfer voice from frequency domain onto mel-frequency domain. The triangular mel-filters in the filter bank are placed in the frequency axis and given different weight. The GTCC used maximum frequency, minimum frequency and number of channels to calculate overlap level. The overlap level is used to find a frequency center. Each Gamma distribution is given different weight to cover the spectrum.
In this thesis, a mixed-type feature extraction algorithm base on the three feature characteristics is proposed. Different feature extraction method is given different weights. The cepstral mean and variance normalization technique are also used for the mixed-type feature extraction algorithm. We also use HMM(Hidden Markov model) to compare the probability and to determine the threshold value.
From the experiment, it shows that using the mixed-type feature extraction algorithm is better than using a sigle feature extraction alone. In the text “1234” experiment, the verification rate for man is increasing by 14.2% and for female is increasing by 9.34%. In the text “ABCD” experiment, the verification rate for man is increasing 17.73% and for female is increasing 15.87%.
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