Matching Feature Distributions for Robust Speaker Verification

Transcription

1 Matching Feature Ditribution for Robut Speaker Verification Marhalleno Skoan, Daniel Mahao Department of Electrical Engineering, Univerity of Cape Town Rondeboch, Cape Town, South Africa Abtract In thi work we improve the performance of a peaker verification ytem by matching the feature vector ditribution obtained when training and teting the ytem. In particular, we perform eperiment uing peech that ha been degraded by telephone tranmiion. Speaker Verification eperiment are performed on the NIST 2000 databae. Significant improvement, above the baeline, are reported. Inde Term Speaker verification, Hitogram Equalization, Gauian miture model S. INTRODUCTION PEAKER verification (SV) i concerned with verifying that an individual i who he/he claim to be. In ideal condition peaker verification ytem perform etremely well. However, a oon a thee ytem are epoed to real-world condition, their performance degrade coniderably [4]. From a tatitical point of view, thee degradation in performance can be attributed to the mimatch between a particular peaker training and teting data ditribution caued by the epoure to real-world condition. In thi work, we improve SV performance by uing a technique that ha it origin in digital image proceing. The technique i known a hitogram equalization and i ued here to optimally minimize the mimatch between training and teting ditribution. Eperiment are performed on the telephone degraded NIST 2000 peech databae. Large improvement, above the baeline ytem, are reported. In addition, we how that hitogram equalization outperform two commonly ued normalization technique namely, ceptral mean normalization and mean and variance normalization. Following thi a likelihood ratio tatitic (X) i computed a the ratio (or difference in the log domain) of thee core. Thi value i then compared to a deciion threh to determine whether to accept or reject the current identity claim. Figure : A typical peaker verification ytem An SV ytem can make two type of error, i.e. it can falely accept impoter (FA) and falely reject true identity claim (FR). In practice, a detection error tradeoff (DET) curve i ued to illutrate the tradeoff between FA and FR error a the deciion threh i adjuted. The equal error rate (EER) i the point on a DET curve where FA FR and i ued and a a ingle performance indicator for thee two type of error. Another performance indicator that i often ued in peaker verification reearch i the detection cot function (DCF) [2, 7]. The i DCF i the weighted arithmetic mean of the FA and FR rate and i defined a 2. AN OVERVIEW OF SPEAKER VERIFICATION There are many paper that provide etenive overview of peaker recognition reearch (eg [, 2, 3, 4]). Thi ection ummarize ome of the concept dicued in thee paper. Fundamentally, an SV ytem need to make a 2-cla deciion. That i, to either accept or reject the current identity claim. Figure depict a typical SV ytem. Here the ytem mut decide whether the input peech ignal better matche a model of the claimed peaker or a background model of non-claimant peaker (impoter). Feature etracted from the front-end proceing unit are compared to the claimed peaker model and to the background model. The minimum value of the DCF i uually computed over all operating point (a the deciion threh i varied). 3. HISTOGRAM EQUALIZATION In many pattern recognition tak, improvement in performance can be epected if one reduce the mimatch between training and teting condition. In peaker recognition (SR) ytem thi mimatch can to a large etent be attributed to varying ambient condition, peech acquiition equipment and tranmiion

2 channel [3]. One way of reducing thi mimatch i by defining tranformation that normalize feature ditribution obtained during the training and teting of an SR ytem. Two uch tranformation are ceptral mean normalization (CMN) and mean and variance normalization (MVN). CMN i a channel compenation technique that ha uccefully been ued to reduce the convolutional effect of telephone channel on input peech ignal [5]. CMN however, alo ha the dual effect of normalizing the mean of each peaker training and tet data ditribution [5]. It doe thi by uing the following tranformation new µ. () MVN, on the other hand, ue the tranformation given in equation (2) to normalize not only the mean but, the variance of thee ditribution a well [6] Uing equation (4), the relationhip between the cumulative probabilitie aociated with p 0 ( 0 ) and p ( ) i given by 0 C ( ) p ( ) d 0 C ( ) C ( ) T ( 0 ) C ( ) C dg( ) p ( G( )) d 0 d p ( ) d (5) ( ) C ( ) C ( T ( )) 0 Thu the tranformation T( 0 ) can be obtained a T ( ) C ( C ( )) (6) 0 new σ. (2) µ In equation () and (2), µ i the global mean of the variable for a particular utterance, wherea σ i the tandard deviation. However, thee technique are linear and can thu not adequately compenate for the non-linear effect caued by telephone tranmiion. To thi end, a technique known a Hitogram Equalization (HEQ), which i ued etenively in digital image proceing [7] and, which ha recently been applied to peech recognition with great ucce [8, 9], i applied in thi reearch. The aim of HEQ i to completely match the ditribution of the training and tet data, not jut the mean and/or variance (like CMN and MVN) [0]. It doe thi by non-linearly tranforming the probability ditribution of a particular peaker feature vector, obtained during training and teting, into a erence ditribution. The formulation of HEQ i a follow [, 2, 3, 4]: Let 0 be a one-dimenional variable with a probability ditribution p 0 ( 0 ). Let T( 0 ) be a ingle-valued and monotonically increaing tranformation that convert the probability ditribution p 0 ( 0 ) into a erence probability ditribution p ( ). In other word, it i a tranformation that make the probability of finding 0 in a differential range d 0 equal to the probability of finding in the correponding range d i.e. where C i the invere of the cumulative ditribution function of the erence probability denity function (PDF). For practical implementation only a finite number of obervation are available. A a reult, cumulative hitogram intead of cumulative probabilitie are ued. Thi i the reaon that the tranformation i called hitogram equalization and not probability ditribution equalization. The tranformation in equation (6) cannot however be eaily be applied to the multidimenional feature vector obtained from the ignal proceing front-end of peaker recognition ytem. A a reult, it i aumed that the all the dimenion of the feature pace are independent. Under thi implifying aumption, the tranformation can be applied to each feature pace dimenion independently. A graphical illutration of the tranformation i depicted in the figure 2. It how how the cumulative hitogram of the original variable and the tranformed variable (the erence cumulative hitogram) can be ued to perform the tranformation. Here each tet/training et value 0 i replaced the value that correpond to the ame point in the erence cumulative hitogram. Thi illutration how that HEQ i computationally attractive a it can be implemented by uing a imple look-up table. p ( ) d p ( ) d (3) 0 Thu the tranformation T( 0 ) modifie the original probability ditribution p 0 ( 0 ) according to the epreion d dg( ) p ( ) p ( ) p ( G( )) (4) 0 0 d d where G( ) i the invere tranformation of T( 0 ). Figure 2: The hitogram equalization tranformation

3 4. THE SPEECH DATABASE Moreno [5] tate that both tationary and non-tationary noie can be encountered in a telephone network. Stationary noie appear in the form of low frequency tone-like ignal, or white noie caued by thermal and other phyical phenomena. He goe on to tate that thee ingle frequency noie can be produced by the harmonic power line and by ignaling tone that get tranmitted by error through the telephone channel. Nontationary noie on the other hand can be attributed to click and other tranient phenomena caued by intermittent connection. A a reult, evaluating hitogram equalization on peech degraded by telephone tranmiion will give one a true idea of it ability to compenate for both linear and non-linear ditortion. In a previou contribution [20], we evaluated HEQ on a peaker identification tak uing the NTIMIT databae. Thi databae contain phonetically rich peech that wa captured in a ound booth during a ingle eion. The peech wa then tranmitted through a carbon-button telephone handet and recorded over local and long ditance telephone loop [2]. Although HEQ wa hown to outperform CMN and MVN, the effect of converational-like peech, different telephone handet and variou period of intereion could not be evaluated uing thi databae. In thi work we evaluated the performance of CMN, MVN and HEQ on the NIST 2000 peaker recognition evaluation databae [6, 7]. Thi databae include converational telephone-quality peech taken from the Switchboard 2 corpu. The tet egment are recorded from call made from a telephone number that i different from the one ued to enroll. Theore, all tet utterance may be conidered to be collected uing a different handet than the one ued for training the peaker model. Each peaker model i trained uing a ingle two minute eion of peech, while teting utterance range between 5 and 45 econd. Thi databae allow one to evaluate peaker verification ytem under very challenging real-world condition a the peech, in addition to being degraded by telephone tranmiion, i alo affected by the ue of different handet, different period of intereion, converational peech and different tet egment length. We ued thi databae to perform 56 true peaker trial and 550 impoter trial (all trial conited of male peaker only). 5. EXPERIMENTAL RESULTS 5.. The baeline ytem In thi work the front-end proceing unit etract mel-frequency ceptral coefficient (MFCC) from the input peech ignal. Thee feature are aimed at emulating the pectral compreion applied by the human auditory ytem to an incoming peech ignal [3]. MFCC are pectrum-baed feature and are ued here a a reult of the peech pectrum having been hown to be very effective in peaker recognition (SR) reearch [2]. Thi i a a reult of it ability to provide an adequate repreentation of an individual vocal tract tructure, which i one of the main peaker dependent characteritic that SR ytem ue to dicriminate between peaker []. The MFCC were generated a follow: the incoming peech ignal wa firt multiplied by overlapping Hamming window which divided it into a equence of 20m frame with an overlap of 0m between frame. Thee peech frame were then Fourier tranformed into the frequency domain where a equence of logmagnitude pectra were computed. To obtain the mel-frequency ceptral coefficient, thee log-magnitude pectra were filtered by a bank of mel-caled triangular filter ditributed over a bandwidth of 0Hz to 3800Hz. The output of the filterbank where then dicrete coine tranformed into 30 dimenional feature vector. In the ubequent eperiment, CMN, MVN and HEQ were applied at thi tage to modify the ditribution of thee feature vector. In order to model the ditribution of feature vector obtained for each peaker, we ued Gauian miture model (GMM) [4, 8]. A GMM can be viewed a a non-parametric, multivariate PDF model that that i capable of modeling arbitrary ditribution and i currently the mot dominant method of modeling peaker in peaker recognition reearch. The GMM of the ditribution of feature vector for peaker S i a weighted linear combination of M unimodal Gauian denitie b i (), each parameterized by a mean vector µ i and a covariance matri i. Thee parameter are collectively repreented by the notation λ { p,, } for i,, M (7) i i i where p i are the miture weight atifying the contraint M pi (8) i For a feature vector, the miture denity for peaker S i computed a where M λ p i i b i p( ) ( ) (9) b ( ) ep( ( ) ' ( )) (0) i D / 2 / 2 i i i (2 π ) i 2 Given a equence of feature vector X {, 2,, T }, which are aumed to be independent, the log-likelihood of a peaker model i given by L ( X T ) log p ( X λ ) log ( ) t t T p λ () For peaker verification, equation () i computed for the claimed peaker model a well a for the background model of non-claimant peaker. The difference between thee value i termed the likelihood ratio (X) and i ubequently compared to a threh to determine whether to accept ((X) ) or reject ((X) < ) the identity claim [4]. In thi work we ued GMM with 64 miture to model each peaker. Thee GMM were obtained from well-trained a background model with a form MAP adaptation according to the work done in [9] The effect of CMN, MVN and HEQ Thi ection evaluate the performance of all the feature normalization technique dicued in ection 3. The variou tatitic for thee technique (uch a the mean, tandard deviation, probability ditribution and cumulative ditribution) were etimated on an utterance by utterance bai.

4 Alo, we choe a Gauian PDF with zero mean and unity variance a the erence PDF for the HEQ technique. Table diplay the performance of CMN, MVN and HEQ on the male portion of NIST 2000 databae. Compenation Technique Equal Error Rate Relative Improvement Minimum DCF No compenation 3.35% CMN 24.57% 2.63% MVN 0.76% 65.68% HEQ 0.6% 67.59% Table : The effect of the feature normalization technique Table clearly illutrate that HEQ perform better than both MVN and CMN but, that MVN outperform CMN. Thi reult i to be epected a HEQ can be viewed a an etenion of MVN which, in turn, can be viewed a etenion of CMN. Thi reult emphaize HEQ ability to compenate for non-linear ditortion of the probability ditribution of the feature vector (a dicued in ection 3) which cannot be eliminated by linear method uch a MVN and CMN. However, from table it can be een that normalization of the variance of the training and teting ditribution account for the larget improvement in performance and that normalization of other moment improve performance only lightly. The trend of the reult obtained in thi reearch correpond to thoe reported in [9] and [0] which ue CMN, MVN and HEQ to improve the performance of peech recognition ytem in noiy environment. In figure 3 we how the ignificant improvement that can be obtained by minimizing the mimatch between training and teting ditribution when peech i obtained in advere environment. Figure 3: The improvement obtained when applying CMN, MVN and HEQ to minimize the mimatch between training and teting ditribution 6. CONCLUSION In thi work we have hown that hitogram equalization i very effective in compenating for both linear and non-linear effect caued by the variou noie ource encountered in a telephone network. In particular, hitogram equalization ability to match training and teting ditribution improved peaker verification performance above the baeline by over 67%. 7. REFERENCES [] D.A. Reyn, An overview of automatic peaker recognition technology, Proceeding of IEEE ICASSP, 4, pp , [2] G.R. Doddington, M.A. Przybocki, A.F. Martin and D.A. Reyn, The NIST peaker recognition Evaluation overview, methodology, ytem, reult, perpective, Speech Communication 3, pp , [3] J. Campbell, Speaker Recognition: A Tutorial, Proc. IEEE, Vol.85, No.9, pp , September 997. [4] D.A. Reyn, Automatic Speaker Recognition Uing Gauian Miture Speaker Model, MIT Lincoln Laboratory Journal, Vol. 8, No. 2, pp , 995. [5] D.A. Reyn and R.C. Roe, "Robut Tet- Independent Speaker Identification Uing Gauian Miture Speaker Model". IEEE tranaction on Speech and Audio Proceing, vol.3, No., January 995. [6] R. Duncan, "A decription and comparion of the feature et ued in peech proceing", Miiippi State Univerity, [7] H.D. Cheng and X.J. Shi, "A imple and effective hitogram equalization approach to image enhancement", Digital Signal Proceing 4, pp.58 70, [8] S. Molau, M. Pitz, and H. Ney, "Hitogram Baed Normalization in the Acoutic Feature Space," Proc. of ASRU, December 200. [9] A. de la Torre, J. C. Segura, M. C. Ben itez, A. M. Peinado, and A. Rubio, Non-linear tranformation of the feature pace for robut peech recognition, Proc. ICASSP, pp , [0] S. Molau, F. Hilger and H. Ney, Feature Space Normalization in Advere Acoutic Condition, Proc. IEEE International Conference on Acoutic, Speech, and Signal Proceing, Vol. I, pp , Hong Kong, China, April [] A. de la Torre, A.M. Peinado, J.C. Segura, J.L. Perez, C. Benitez, A.J. Rubio: 'Hitogram equalization of the peech repreentation for robut peech recognition '. IEEE Tranaction on Speech and Audio Proceing, Article In Pre [2] S. Molau "Normalization in the Acoutic Feature Space for Improved Speech Recognition". PhD Diertation, Aachen, Germany, February 2003 [3] Ceptral domain egmental nonlinear feature tranformation for robut peech recognition. J. C. Segura, C. Benítez, Á. de la Torre, A. J. Rubio, J.

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