Feature Extraction and Test Algorithm for Speaker Verification

Transcription

1 Feature Extracton and Test Algorthm for Speaker Verfcaton Wu Guo, Renhua Wang and Lrong Da Unversty of Scence and Technology of Chna, Hefe Abstract. In ths paper we ntroduce two methods to mprove text-ndependent speaker verfcaton. In feature extracton process the feature vectors of voced speech and unvoced speech ndependently. In test process, the test speech s adapted to a new model nstead of calculatng the log-lkelhood, then the Mahalanobs Dstances among the UBM model, the speaker model and the test speech model are calculated. These three models dstances formed a trangle. The angle of the models can be obtaned as the test scores. Further more the scores of the log-lkelhood and the angle of the models can be fused to mprove performance. When we employ the proposed algorthms, the EER of the speaker verfcaton can be reduced by 20% compared wth the baselne system. 1 Introducton In recent years, the Gaussan Mxture Model(GMM) s state-of-the-art technology n text-ndependent speaker recognton system[2, 6, 7]. For example, a GMM-based system developed by MIT Lncoln Laboratory whch employs MAP adaptaton of speaker models from a Unversal Background Model (UBM) and handset-based score normalzaton (HNORM) has been the bass of the top performng systems n the past several NIST Speaker Recognton Evaluatons. The Mel-frequency Cepstral Coeffcent[1] (MFCC) s adopted as the feature vector n most speaker verfcaton. In the standard MFCC approach, the voced vectors and unvoced vectors are warped n the framework of Gaussanzaton[7] frame by frame. But the voced speech s more steady both n tme and frequency doman and embody more nformaton of the speaker, the recognton of speaker reles more on the voced than the unvoced[11], n ths paper the voced vectors and unvoced vectors are warped ndependently. In the archtecture of GMM_UBM, the log-lkelhood rato for a test sequence of feature vectors X s computed to decde whether the speech s uttered by the hypotheszed speaker. It's natural to fnd some other method whch can optmzes the test algorthm. In ths paper, we propose a novel test method for speaker verfcaton referred as angle of models dstances. In the method of angle of models dstances, the feature vectors of test sequence are adapted to a new model through one pass MAP[2], so we get three models, the UBM model, the hypotheszed speaker model and the test sequence model. We can say that f the test sequence model s closer to the hypotheszed speaker model than to the UBM model, then the test sequence has more lkelhood to be spoken by the hypotheszed speaker. We use the angle of mod-

2 els dstances to scale the dstance of three models. Detaled dscusson wll be ncluded n Secton 4. The remander of ths paper s organzed as follows. Secton 2 descrbes the GMM_UBM model and ts log-lkelhood rato scores test algorthm. Secton 3 descrbes the proposed feature warped process. In Secton 4 the angle of the models dstances as the test algorthm s descrbed. Ths secton also presents the fuson of the angle and the log-lkelhood rato scores. Secton 5 presents experments and results of the proposed algorthms usng the NIST SRE 2002 corpora. Fnally, concluson s gven n Secton 6. 2 GMM_UBM The baselne text-ndependent speaker verfcaton system dscussed n ths paper s shown n Fgure 1 and fully descrbed n[2]. The typcal UBM model tranng method has EM[3] algorthm. The speaker model s then derved va the UBM model through the avalable enrollment speech and one pass MAP estmaton. For verfcaton, the log-lkelhood score of the nput speech sequence s computed aganst the UBM model and the hypotheszed speaker model, the dfference s taken and compared to a threshold to decde whether to accept or reject the putatve speaker clam. Speaker Model + Speech MFCC + - Λ UBM Model Fg. 1. Structure of GMM_UBM, the test sequence score s computed through the dfference of the log-lkelhood rato scores of the Speaker model and the UBM model. For a D-dmensonal speech sequence feature vector, x, the mxture densty used for the lkelhood functon s defned as: M p( x λ) = w p ( x) (1) = p x x x (2 π ) 2 ' 1 ( ) = exp{ ( μ )( ) ( μ )} D /2 1/2 (2)

3 M D μ and stand for the number of Gaussan mxtures dmenson mean and covarance matrx respectvely. If the UBM model and the hypotheszed speaker model are represented by λubm and λ speaker respectvely, then the log-lkelhood rato for a test sequence of feature vectors X s computed as: 1 Λ ( X) = {log p( X ) log p( X )} T T λspeaker λubm (3) = 1 3 Feature extracton After MFCC extracton, both Cepstral Mean Subtracton (CMS)[1 2] and RASTA[5] flterng are used to remove lnear channel convolutonal effect on the cepstral features. Feature Gaussanzaton[7] s adopted to mprove performance. The voced speech s more lke a perodc sgnal whle the unvoced speech s more lke a random sgnal. The voced speech embodes more nformaton of the speaker, and the recognton of speaker reles more on voced than the unvoced. In ths paper we treat the voced vectors and unvoced vectors ndependently. The procedure s depcted n Fg. 2. Fg. 2. The procedure of feature extracton, voced and unvoced vectors treated ndependently. The proposed feature extracton has the same procedure as the stand process except for the Gaussanzaton and the save process. In the proposed process, the voced vectors and unvoced vectors are transformed ndependently n the framework Gaussanzaton. After Gaussanzaton, the voced vectors are saved to the destnaton fle twce whle the unvoced vectors only once because the voced speech has more nformaton for verfcaton. 4 Angle of model dstance If we don t compute the log-lkelhood rato scores of the test sequence, nstead we adapt the test sequence to a new model va the UBM model, then we get three models:

4 the UBM model the test sequence model and the hypotheszed speaker whch s also adapted va the UBM model. All these models are shown n Fgure 3. Fg. 3. Angle of models dstances. The test sequence s adapted to a new model va the UBM model. There are many methods to descrbe the dstance between two Gaussan mxtures, for example, the KL-Dstance[4], but the computaton s very large. The smplest method to descrbe the dstance of two Gaussan pdfs s the Mahalanobs Dstance. For two Gaussan pdfs g and h, the Mahalanobs Dstance s: (4) J g h μ μ μ μ T 1 (, ) = ( g h) [ + ] ( g h) g h The μ and stand for mean and covarance matrx respectvely. The dagonal covarance matrx s always adopted n speaker verfcaton, so equaton (4) can be smplfed to equaton (5), μ, v and D stand for mean,varance and dmenson respectvely. D 2 ( μg μh ) 2 2 = 1 ( vg + vh ) J( g, h) = (5) For Gaussan mxtures case lke (1), f two Gaussan mxture models are all adapted from the same UBM model, and the weghts of the mxtures are left changed, equaton (6) can be derved: J( g, h) = M D 2 ( μgj μhj ) wu 2 2 = 1 j= 1 ( vgj + vhj ) (6) μ and v stand for mean and covarance derved from the UBM model and w s the same as the UBM model. In test process, the test sequence model can be derved

5 through MAP as the same process as the hypotheszed speaker adaptaton. The Mahalanobs Dstances JUBMTest (, ) J ( UBM, Spea ker) and J( Speaker, Test ) between UBM Speaker and Test sequence models can be obtaned through (6). These there dstances form a trangle. From Fg. 3, we can see that the angle of dstances trangle may be a good parameter to descrbe the close or far among these models. In ths experment the angle between JUBMTest (, ) and J( Speaker, Test) s used as the parameter to descrbe the close or far. The angle can be computed through the laws of cosnes. The bgger the vale of angle θ s, the more lkely to be uttered by the hypotheszed speaker the test sequence s. But we must pay attenton to one mportant ssue. The angle of the models dstances s not entrely the same as the angle of the objects. So n some cases, we can get J ( Spea ker, Test) > J ( UBM, Test) + J ( UBM, Spea ker) and such cases that do not satsfy the law of trangle nequalty. The vale of θ s set to zero when encountered wth ths stuaton, whch means the test sequence s not uttered by the hypotheszed speaker. If the log-lkelhood rato scores of the test sequence can also be computed, we can further fuse the angle of the models dstances wth the log-lkelhood rato score. These two functons are both ncreasng functons, the value of angle of the models dstances θ les n the closed nterval[0, π ], the value of log-lkelhood rato score Λ les n a wde nterval, but proportonal to the θ, so we can fuse them drectly usng equaton (7) Γ ( x) = α1λ ( x) + αθ 2 ( x), α1+ α2 = 1 (7) 5 Experments 5.1 Evaluaton Corpus Speaker verfcaton experments are carred on the 2002 NIST sngle Speaker verfcaton Evaluaton corpus. Ths corpus conssts of 139 male target speakers and 191 female target speakers. There are about 3500 target trals n matched and msmatched condtons respectvely, and same-sex mpostor trals. 5.2 System descrpton In the experments, the VAD detector[8] can dscard about 25%~30% slence frames from data fles. The speech s pre-emphaszed wth a factor of 0.97 and segmented nto frames by a 25-ms Hammng wndow progressng at a 10-ms frame rate. Each

6 speech sgnal s parameterzed usng the frst 13 MFCCs, and ther frst and second dervatves, formng a 39-dmenson feature vector. CMS and RASTA flterng are used to remove lnear channel convolutonal effects. In baselne system, One Gaussanzaton s appled to all the MFCCs. In the proposed feature extracton, Praat 4.0[9,10] s used to decde Voced/Unvoced. The voced frames and unvoced frame are transformed n the framework of Gaussanzaton ndependently and the voced frames are saved to fles twce whle the unvoced frames once. A 1024 GMM female and male UBM models are ndependently traned va the K- MEANS and EM algorthm, a sngle 2048 GMM UBM model s formed by poolng the gender-dependent UBMs. The speaker models are derved from the UBM model va a smplfed MAP adaptaton. If the test algorthm adopts log-lkelhood rato scores, only the means of speaker models are adapted from the UBM model[2]. If the test algorthm adopts angle of the models dstances, the means and varances of speaker models are adapted from the UBM model. The test sequence s fed nto two test system, the frst s the standard log-lkelhood rato scores, the second s the angle of the models dstances. 5.3 Evaluaton results The SV performance can be assessed usng the Equal Error Rate (EER) the Detecton Error Tradeoff ( DET) curve and the Detecton Cost Functon (DCF). For smplcty, the EER s adopted to evaluate the algorthms. Table 1. The EER of speaker verfcaton under all knds of condton System Descrpton System 1:Baselne system, Standard MFCC, Feature Gaussanzaton, log-lkelhood rato scores EER 7.8% System 2:MFCC wth the Voced part doubled, Feature 6.9% Gaussanzaton ndependently, log-lkelhood rato scores System 3:MFCC wth the Voced part doubled, Feature 7.2% Gaussanzaton ndependently, angle of models System 4:Fuson system 2 and system 3 6.3% The EER of the experments s shown n Table 1. It's clear from the results that system 2 outperforms system 1 by about 11% wth the voced part doubled and feature Gaussanzaton ndependently. From system 2 and system 3, we can say that the angle of the models dstances almost has the same effect as the log-lkelhood rato scores n verfcaton. The fuson of the angle of the models dstances and the loglkelhood rato scores outperforms any sngle test algorthms, the EER can decrease about 20% aganst the baselne system. Now let us consder the computaton load of the angle of the models dstances aganst the log-lkelhood rato scores. If we don t consder the MAP adaptaton of the test sequence, the computaton loads are lsted n Table 2. We can see that the

7 computaton load can decrease greatly usng the angle of the models dstances. That s very mportant for real-tme speaker recognton applcatons. Table 2. The computaton load of the angle of the models dstances aganst the log-lkelhood rato scores, T M D are defned n equatons (1)(2)(3) log-lkelhood rato angle of the models scores dstances ADD/SUB(quantty) T M D M D MUL/DIV(quantty) T M D M D EXP(quantty) T M 0 LOG(quantty) T M 0 If we consder the MAP adaptaton of the test sequence, the angle of the models dstances needs a MAP adaptaton whle the log-lkelhood rato score does not need such process. But the smplfed MAP[2] used n speaker recognton has almost the same computaton as the computaton for one pass log-lkelhood rato scores. So compared wth log-lkelhood rato scores, the proposed algorthm needs only half of the computaton n a speaker verfcaton case. In multple speakers cases, f the number of the hypotheszed speakers s N, the computaton of recognton can reduced to 1 f we adopt the angle of the models dstances to replace the log-lkelhood N rato scores. 6 Concluson Ths paper has proposed two approaches to mprove text-ndependent speaker verfcaton task usng GMM_UBM models. The 2002 NIST Speaker Recognton Evaluaton corpus s used to conduct the experments. It s shown n the experments that the proposed methods can mprove the speaker verfcaton. Furthermore, the angle of the models dstances outperforms the log-lkelhood rato scores from the vew of computaton load. References 1. Steve Young: The HTK Book. Ver 3.0, July Douglas A. Reynolds, Thomas F. Quater and Robert B. Dunn: Speaker verfcaton usng adapted Gaussan mxture models. Dgtal Sgnal Processng 10, Academc Press(2000) 19-41

8 3. Dempster, A., Lard, N., and Rubn, D.: Maxmum lkelhood from ncomplete data va the EM algorthm. Journal. Roy. Stat. Soc. 39 (1977) Jacob Goldberger, Shr Gordon, Hayt Greenspan,An Effcent Image Smlarty Measure Based on approxmatons of KL-Dstance Between Two Gaussan Mxtures, ICCV03,pp , Hermansky, H., Morgan, N., Bayya, A, and Kohn, P.: RASTA-PLP Speech Analyss. ICSI Techncal Report TR , Berkeley, Calforna. 6. Fr ed erc Bmbot, Jean-Franc os Bonastre: A Tutoral on Text-Independent Speaker Verfcaton. EURASIP Journal on Appled Sgnal Processng 2004:4, Bng Xang, Upendra V. Chaudhar, Jˇr ı Navr atl, Short-tme Gaussanzaton for robust speaker verfcaton, Proc. IEEE ICASSP 02, 2002, vol. 1, pp LORI F. LAMEL, LAWRENCE R. RABINER, An Improved Endpont Detector for Isolated Word Recognton, IEEE Trans. on Acoustcs, Speech, and Sgnal Processng, VOL. ASSP- 29, NO. 4, AUGUST ww.praat.org 10. Paul Boersma, Accurate Short-term Analyss of the Fundamental Frequency and the Harmoncs-to-Nose Rato of a Sampled Sound,Insttute of Phonetc Scences, Unversty of Amsterdam, Proceedngs 17 (1993), Sachn S. Kajarekar, Hynek Hermansky, Speaker Verfcaton Based on Broad Phonetc Categores. In ODYSSEY-2001,