40 3 Journal of South China University of Technology Vol. 40 No Natural Science Edition March

HTML
DOWNLOAD

Μέγεθος: px

Εμφάνιση ξεκινά από τη σελίδα:

Download "40 3 Journal of South China University of Technology Vol. 40 No Natural Science Edition March"

Πάνδαρος Μαγγίνας
8 χρόνια πριν
Προβολές:

1 40 3 Journal of South China University of Technology Vol 40 No Natural Science Edition March X * MFCC K-L K-L MFCC K-L 46 61% 42 25% 39 68% 36 36% K-L TN912 3 doi /j issn X SR PMC CMN CMN 2 6% Mel 1 3% MFCC CMN RAS- TIMIT TA WM 2 0 6% * ZM hejun_723@ 126 com eeyxli@ scut edu cn

2 3 107 k MFCC F i k = f 1k i f2k i frk i T frk i i r k F NSFT m F NSFT MFCC Mel F 1 1 F 1 2 F 1 n F 2 1 F 2 2 F 2 n F NSFT = 1 F m 1 F m m 2 Fn 1 2 CMN 1 /ɑ / /ɑ / 1 a 4 8 khz 6 0 khz 1 6 ~ 4 8 khz 0 ~ 1 6 khz 1 b 6 0 ~ 8 0 khz MFCC K-L Kullback-Leibler Divergence 10 MFCC GMM MFCC MFCC MFCC NSFT s i i s i n MFCC 1 /ɑ / /ɑ / F i = F 1 i F i k F i n F i k i Fig 1 Spectra of abnormal speech /ɑ / and normal speech /ɑ /

3 ~ 4 8 khz 1 17 s ~ 1 6 khz 15 ~ 20 s MFCC 1 2 b K-L 10 MFCC K-L 12 MFCC K-L 3 ~ 5 s 826 MFCC 2 a MFCC K-L NSFT K-L Table 1 K-L distances from one abnormal speech and 826 abnormal speeches to NSFT K-L p x q x K-L D K-L p q = p x lg p x q x dx 2 12 MFCC d k K-L q k p k = N q k x lg q k x 4 Fig 2 12-order MFCC feature probability distribution of i = 1 p k x normal speech and one abnormal speech N 2 p x q x 2 D K-L p q = N i = 1 p x lg p x q x 3 F k F NSFT k MFCC p k F k s c MFCC k q k k MFCC F NSFT k

4 3 109 F NSFT K-L W i E 7 W k K-L D c K-L = d 1 K-L d k K-L d n K-L 5 d k K-L k MFCC 3 d k K-L k MFCC 863 k K-L MEEI W k K-L = a K-L + εδ x /d k K-L 6 δ x δ x = 1 x median Dc K-L { - 1 x < median D c K-L d E x i x j = M x im' m' = 1 槡 2 - x jm' 8 M SNR 37 ~ 55 db yep120 s c MFCC F c F c i A i F NSFT i B i A i j B i d j A i j B i d j A i j B i = min ( M' A i j - B i k 9 ) k = 1 M' B i 60 d i E A i B i = N' d j A i j B i 10 4 j = 1 N' A i PANSD F NSFT D c E = d 1 E d i E d n E 11 W i E = a E + εδ x /d i E 12 a E 12 PANSD PANSD ~ median D c K-L D c K-L a K-L 8 ~ 16 ε F Nk = W k K-LF Ok F Nk k MFCC 1253 F Ok k PANSD m' ~ 35 Toplux TVP khz 16 3 ~ 5 5 ~ 20 cm 20 ~ 25 min PANSD 700 min PANSD ~ 4 s d i E i F NSFT 826 i i 15 ~ 20 s 1 ~ 2 min GMM WAV Cooledit Pro khz

5 ms 16 ms 24 2 MFCC Table 2 Comparison of speaker recognition rates for abnormal speech % ~ 3 min K-L-W E-W MFCC 2 3 K-L-W E-W 9 1 K-L Fig 3 Flowchart of the proposed algorithm 4 K-L-W E-W ε = 0 5 K-L-W 46 61% 10 25% E-W % 6 93% K-L-W E-W E-W 98 56% % 98 38% K-L 98 02% K-L-W E-W 5 4 Fig 4 Influence of weighting parameter on speaker recognition rate for abnormal speech K-L GMM K-L-W E-W K-L-W E-W GMM K-L

6 % 42 25% Processing Magazine Togneri R Pullella D An overview of speaker identification accuracy and robustness issues J Circuits and 39 68% 36 36% Systems Magazine Rashid R A Mahalin N H Sarijari M A et al Security system using biometric technology design and implementation of voice recognition system C Proceedings of International Conference on Computer and Communication Engineering Kuala Lumpur IEEE J Yang Ji-cheng He Qian-hua Pan Wei-qiang Modified BIC algorithm of speaker change detection J Journal of South China University of Technology Natural Science Edition J J Annals of Mathematical Statistics Zhang Lei Han Ji-qing Wang Cheng-fa Research progress of stress speech processing J Acta Electronic Sinica Alpan A Maryn Y Kacha A et al Multi-band dysperiodicity analyses of disordered connected speech J Speech Communication Maciel C D Pereira J C Stewart D Identifying healthy and pathologically affected voice signals J IEEE Signal 7 Garner Philip N Cepstral normalisation and the signal to noise ratio spectrum in automatic speech recognition J Speech Communication Yang Hong-wu Liu Ya-li Huang De-zhi Speaker recognition based on beighted Mel-cepstrum C Proceedings of the Fourth International Conference on Computer Sciences and Convergence Information Technology Seoul BIC IEEE Weng Zufeng Li Lin Guo Donghui Speaker recognition using weighted dynamic MFCC based on GMM C Proceedings of International Conference on Anti-Counterfeiting Security and Identification in Communication Chendu IEEE Kullback S Leibler R On information and sufficiency 11 You Chang Huai Lee Kong Aik Li Haizhou GMM-SVM kernel with a bhattacharyya-based distance for speaker recognition J IEEE Transactions on Audio Speech and Language Processing Ferrante A Ramponi F Ticozzi F On the convergence of an efficient algorithm for kullback-leibler approximation of spectral densities J IEEE Transactions on Automatic Control Speaker Recognition Algorithm for Abnormal Speech Based on Abnormal Feature Weighting He Jun Li Yan-xiong He Qian-hua Li Wei School of Electronic and Information Engineering South China University of Technology Guangzhou Guangdong China Abstract As the commonly-used weighting algorithm is inefficient in tracking the abnormal feature of abnormal speech a speaker recognition algorithm for abnormal speech is proposed based on the abnormal feature weighting In this algorithm first a feature template of normal speech is established by computing the probability distribution of MFCC features of each order in a large number of normal speech samples Then the K-L distance and the Euclidean distance are used to measure the differences between a given test speech and the normal speech templates and to further determine the K-L and the Euclidean weighting factors Finally the two weighting factors are used to weight the MFCC features of the test speech and the weighted MFCC features are input in the Gaussian mixture model for the speaker recognition with abnormal speech Experimental results show that the global recognition rates of the speaker recognition algorithms based on the K-L weighting and the Euclidean weighting are respectively 46 61% and 42 25% while those of the algorithms with and without the weighting of speaker recognition contribution of each order feature are respectively only 39 68% and 36 36% Key words abnormal speech speaker recognition abnormal feature weighting K-L distance weighting factor

Σχετικά έγγραφα

Nov Journal of Zhengzhou University Engineering Science Vol. 36 No FCM. A doi /j. issn

Nov Journal of Zhengzhou University Engineering Science Vol. 36 No FCM. A doi /j. issn 2015 11 Nov 2015 36 6 Journal of Zhengzhou University Engineering Science Vol 36 No 6 1671-6833 2015 06-0056 - 05 C 1 1 2 2 1 450001 2 461000 C FCM FCM MIA MDC MDC MIA I FCM c FCM m FCM C TP18 A doi 10