COMPARATIVE STUDY OF LPCC AND FUSED MEL FEATURE SETS FOR SPEAKER IDENTIFICATION USING GMM- UBM

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1 Internatonal Journal of Electroncs and Communcaton Engneerng & Technology (IJECET) Volume 6, Issue 9, Sep 015, pp. 8-96, Artcle ID: IJECET_06_09_010 Avalable onlne at ISSN Prnt: and ISSN Onlne: IAEME Publcaton COMPARATIVE STUDY OF LPCC AND FUSED MEL FEATURE SETS FOR SPEAKER IDENTIFICATION USING GMM- UBM Anagha S. Bawasar Dept. of Electroncs & Telecommuncaton, M. E. S. College of Engneerng, Pune, Inda Prabhaar N. Kota Dept. of Electroncs & Telecommuncaton, M. E. S. College of Engneerng, Pune, Inda ABSTRACT Bometrcs dentfers are typcally measurable characterstcs used to label and descrbe the ndvdual respectvely. Bometrc dentfers are the combnaton of both physologcal and behavoral characterstcs. The physologcal characterstcs nclude the characterstcs related to the shape of the body. There are varous examples for physologcal characterstcs but not lmted. Examples nclude fngerprnt, palm, hand geometry, rs recognton, and retna. Behavoral characterstcs are related to the pattern behavor of a person ncludng but not lmted to typng rhythm and voce. Bometrc system technology s now a day s a well-furnshed technology, t analyzes human body characterstcs. It s also nown as one of the actve bometrc tass. There s much speech related actvtes such as language recognton, speech recognton, and speaer recognton respectvely. Speaer recognton superfcally defnes as to dentfy the accurate speaer from the group of varous people. It s a very broad term and s further classfed as speaer dentfcaton and speaer verfcaton. The paper s concentratng on the term speaer dentfcaton. The man am s to dentfy the accurate speaer from the gven speech samples. These samples are obtaned by extractng features and are used for modelng purpose. Standard database TIMIT s beng used for dentfcaton. The paper comprses of varous algorthms for feature extracton, they are Mel Frequency Cepstral Coeffcents (MFCC), Inverse Mel Frequency Cepstral Coeffcent (IMFCC) and lnear predctve Cepstral Coeffcents (LPCC). The term Fuson came from the combnaton of the two algorthms namely MFCC and IMFCC. The 8 edtor@aeme.com

2 Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM comparson s made among the results of Fuson and LPCC respectvely. From the result, t s seen on an average Fuson s better than LPCC. Index Terms: Gaussan Mxture Models (GMM), Inverted Mel Frequency Cepstral Coeffcents (IMFCC), Lnear Predctve Cepstral Coeffcents (LPCC), Mel Frequency Cepstral Coeffcents (MFCC), Unversal Bacground Model (UBM) Cte ths Artcle: Anagha S. Bawasar and Prabhaar N. Kota. Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM, Internatonal Journal of Electroncs and Communcaton Engneerng & Technology, 6(9), 015, pp INTRODUCTION Nowadays varous bometrcs systems are there. In last decades, an ncreasng nterest n securty system has rsen. For the securty purpose, t ncludes varous bometrc schemes. Bometrcs refers to technologes that measure and analyzes human body characterstcs. There are many bometrc methods exstng n the world, they are face recognton, eye retna and rs recognton, fngerprnt, DNA, hand measurements etc. for authentcaton purpose. These are the one of the well-nown bometrc method, addng to ths lst one of the well-nown method s Speech sgnal processng. Speech s one of the natural forms used n communcaton. Speech recognton has applcaton n voce dentfcaton n ordnary personal computers to bometrc and forensc applcatons. Recently the development has been seen n a securty system. There are two man technques n speech processng, one s speaer recognton and the other s speech recognton, n ths paper the man focus s gven on speaer recognton. The speaer recognton s further dvded only speaer dentfcaton and speaer verfcaton. Speaer dentfcaton s the technque n whch not regstered speaer s beng dentfed and Speaer verfcaton a clamed speaer s beng dentfed. The speaer dentfcaton s n a rato of 1: N whle speaer verfcaton s n 1:1 rato respectvely. In ths paper text- ndependent, speaer dentfcaton system s used. In speaer dentfcaton, the specfc characterstcs of voce are beng extracted from the gven sample of voce of speaer nown as feature extracton. After ths, the speaer model s traned and stored nto the system database. The extracton of the voce of speaer yelds us the specfc nformaton of the speaers voce called feature vectors. The speaer vectors represent the specfc nformaton of the speaer whch s based on the sngle or many thngs from the followng: vocal tract, exctaton source, and behavoral trats. All speaer recognton systems use the set of scores to enhance the probablty and relablty of the recognzer. Before feature extracton, the system goes through the pre-processng stage. An mportant role s played by Preprocessng n speaer dentfcaton and helps to reduce the amount of varaton n the database whch does not contan the mportant nformaton about speech; t s consdered to be a good practce. The preprocessng removes the rrelevant nformaton respectvely. The varous algorthms used for feature extracton are Mel-frequency Cepstral Coeffcents (MFCC), Inverse Mel Frequency Cepstral Coeffcents (IMFCC), and Lnear Predctve Cepstral Coeffcents (LPCC). In ths paper, feature extracton s 83 edtor@aeme.com

3 Anagha S. Bawasar and Prabhaar N. Kota done by usng all these above-mentoned algorthms. Investgatons by the researchers fnd out speaer specfc complementary nformaton relatve to MFCC that are called as Inverse Mel Frequency Cepstral Coeffcents (IMFCC) respectvely. Complementary nformaton s used for combnng the score models and for combnng the score models along wth the MFCC and s named as Fused Mel Feature Set. Such models are nothng but the mathematcal representaton of the partcular system [1].The nverse flter ban method s beng used for capturng ths complementary nformaton from hgh-frequency part of the energy spectrum. IMFCC captures the nformaton whch s neglected by MFCC. The respectve features are modeled by usng Gaussan Mxture Model and Unversal Bacground Model (GMM- UBM). All algorthms used n ths paper are based on Gaussan flters only. The results are verfed n standard database TIMIT. The fnal results are the comparson between LPCC results and Fused Mel Feature Set results and accurate results are noted down. The next secton of ths paper s followed by Fused Mel Feature set usng Gaussan Flters and Lnear predctve Cepstral coeffcent usng Gaussan flters. It s followed by comparatve results of both Fused Mel feature Set and Lnear Predctve Cepstral Coeffcent.. FEATURE EXTRACTION AND FILTER DESIGN To represent any speech sgnal n a fnte number of measures s the goal of feature extracton. Features are nothng but the representaton of the spectrum of a speech sgnal n each wndow frame. The Cepstral vectors are derved from a flter ban that has been desgned accordng to some model of the audtory system []. Most of the feature extracton methods use a standard trangular flter. The trangular flters are used for flterng the spectrum of the speech sgnal whch smulates the characterstcs of a human ear. But ths also has some dsadvantages. These are, they gve sharper or crsp partton n an energy spectrum, due to ths some nformaton s lost. In ths paper, Gaussan flters are used. The crsp and sharp transton n an energy spectrum s avoded f we use Gaussan flters nstead of trangular flters. Ths gves results n a smoother adaptaton from one sub band to other. Because of ths adaptve property, there s always one type of correlaton beng mantaned. These correlatons are mantaned from the md ponts of the trangular flters at the base of t as well as from the end ponts of trangular flters. Mathematcal calculatons n Gaussan flters are smple. Hence because of such advantages over trangular flters we use Gaussan Flters. The motvaton for usng Mel-Frequency Cepstrum Coeffcents was due to the fact that the audtory response of the human ear resolves frequences nonlnearly. The mappng from lnear frequency to Mel Frequency s defned as [3]. f 595log (1 f mel ) (1) Where; The subjectve ptch n Mel correspondng to f s f mel, ths frequency n actual measured n Hz. MFCCs are one of the more popular parameterzaton methods used by researchers n the speech technology feld. It has the beneft that t s capable of capturng the phonetcally mportant characterstcs of speech. MFCC are bandlmtng n nature and can easly be employed to mae t sutable for applcatons le a telephone edtor@aeme.com

4 Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM Generally, the feature extracton usng MFCC uses a trangular flter. The trangular flter has some characterstcs le, asymmetrcally taperng whch also do not provde the correlaton between the sub-bands and the nearby spectral components. Because of all ths, nformaton loss occurred there. By usng Gaussan flters, one proft s that t avods drawbacs and losses seen n a trangular flter. Gaussan flters are taperng towards both the end and provde correlaton between sub-bands and ts nearby spectral components [4]. The IMFCC s one of the feature extracton technques. It captures the complementary nformaton present n the hgh-frequency part of the spectrum. The fgure below shows the steps nvolved n feature extracton of both Gaussan MFCC and IMFCC features. Let the nput speech sgnal be y (n), where n=1, M. t represent the preprocessed frame of the sgnal. Frstly the sgnal y (n) s converted to the frequency doman by a DFT whch leads to the energy spectrum. Ths s followed by Gaussan flter ban bloc. Speech Sgnal Pre- FFT Processng MFCC Flter Ban Gaussan IMFCC Flter Ban Gaussan Log 10 () Log 10 () DCT DCT MFCC IMFCC Fgure 1 Steps nvolved n extracton of Gaussan MFCC and IMFCC [5] Mathematcally the equaton for Gaussan flter s wrtten as; g MFCC e ( b) () Where, s coeffcent ndex n the N-pont DFT, b s a pont between the th th trangular flters boundary located at ts base and consdered as mean of Gaussan flter whle the s the standard devaton or square root of varance and can be wrtten as, b 1 b (3) Where; s the parameter where varance s beng controlled. Fgure Flterban desgn [5] 85 edtor@aeme.com

5 Weght Weght Anagha S. Bawasar and Prabhaar N. Kota Two plots n a sngle fgure are shown n above fgure. One s for trangular flter and the other s for the Gaussan flter. Ths plot s made by consderng a sngle value of sgma. Here n ths case by consderng dfferent values for sgma plot can be drawn respectvely. Fg 4 and Fg 5 shows the ndvdual response for Gaussan flter ban of MFCC and IMFCC Frequency (Hz) Fgure 3 Mel scale Gaussan flter ban [6] Frequency (Hz) Fgure 4 Inverted Mel scale Gaussan flter ban [6] Mathematcally, the Gaussan MFCC can be wrtten as, b M s Fs f 1 mel f mel ( f low f ) mel f f f hgh Q 1 mel low M s s a number of ponts n DFT, Fs s the samplng frequency, f low and fhghare low and hgh-frequency boundares of a flterban, Q s the number of flters n the ban and 1 f mel s an nverse of the transformaton. 1 f mel ( f mel ) 700(10 fmel / 595 1) (5) The nverted Mel Scale Flterban structure can be obtaned by just flppng orgnal flterban around the mdpont of frequency range that s beng consdered. ' ( ) q 1 Where, ' ( ) M s 1...(6) s the orgnal MFCC flter ban response. (4) 86 edtor@aeme.com

6 Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM These flter bans are beng forced on the energy spectrum obtaned by tang Fast Fourer transform of the preprocessed sgnal as follow: e gmfcc ( ) Ms 1 Y( ) gmfcc. ( ) Where, () s respectve flter response and Ampltude (7) Y() s the energy spectrum. DFT coeffcent ndex b 1 b b 1 Fgure 5 Response () of a typcal Mel scale flter [5] Q Fnally, DCT s taen on the log flter ban energes {{log[ e( )]} } and the fnal MFCC coeffcents can be wrtten as- C gmfcc m Q l 1 ( 1)] cos[ m( ) ]...(8) Q 1 gmfcc log[ e l0 Q Where; 0 m R1, R s the desred number of Cepstral features. The same procedure for extractng the IMFCC features as well [4] and are denoted as; gimfcc m C Q 1 Q log[ e gimfcc l l ( 1)] cos[ m ( ) ]...(9) l0 Q 3. LINEAR PREDICTIVE CEPSTRAL COEFFICIENTS (LPCC) The predctve coeffcent can be determned by mnmzng the squared dfferences between actual speech samples and lnearly predcted values. Ths set s a unque set of parameters. In practce, the actual predctor coeffcents are never used as t s because of ther hgh varance. These predctor coeffcents are transformed to a more robust set of parameters nown as Cepstral coeffcents. The procedure for extractng the LPCC s same as that of MFCC and IMFCC respectvely. In ths also we are gong to use Gaussan flter ban. Speech Sequence Pre-emphass and hammng wndow Lnear Predctve Analyss Cepstral Analyss LPCC Fgure 6 Bloc dagram of LPCC algorthm [7] 87 edtor@aeme.com

7 Anagha S. Bawasar and Prabhaar N. Kota Pre-emphass and Hammng Wndow The frst bloc s a pre-emphass bloc; the nput sgnal s gven to, the frst step of the algorthm s pre-emphass. The dea of pre-emphass s to spectrally flatten the speech sgnal and equalze the nherent spectral tlt n a speech [8]. Pre-emphass s mplemented by a frst order FIR dgtal flter. The followng equaton shows the transfer functon of the pre-emphass dgtal flter, 1 H p ( Z) 1z (10) Where, alpha s constant, whch has a typcal value of After pre-emphass, the speech sgnal s subdvded nto frames. Ths process s the same as multplyng the entre speech sequence by a wndowng functon, s m [ n] s[ n] w[ n m] (11) Where s[n] s the entre speech sequence, s [n] s a wndowed speech frame at tme m m and w[n] s the wndowng functon. The typcal length of a frame s about 0-30 mllseconds. In the above equaton, m s the tme shft or the step sze of the wndowng functon. A new frame s obtaned by shftng the wndowng functon to a subsequent tme. The amount of shftng s typcally 10 mllseconds. The shape of the wndowng functon s mportant. Rectangular wndow s not recommended snce t causes severe spectral dstorton (leaage) to the speech frames [9]. Other types of wndowng functon, whch mnmze the spectral dstorton, should be used. One of the most commonly used wndows s the Hammng wndow. w[ n] cos N 1 (1) In the above equaton, N s the length of the wndowng functon. After Hammng wndowng, the speech frame s passed to the next stage for further processng. Lnear predctve analyss In human speech producton, the shape of the vocal tract governs the nature of the sound beng produced. The man dea s based on basc speech producton model; t says that vocal tract can be modeled by an all-pole flter. These are nothng but the smple coeffcent of all-pole flter. They are same as smooth envelope of log spectrum of speech. The man dea behnd LPC s that a gven speech sample can have approxmated as a lnear combnaton of the past speech samples. LPC models sgnal s (n) as a lnear combnaton of ts past values and present nput (vocal cords exctaton). If the sgnal wll be represented only n terms of the lnear combnaton of the past values then the dfference between real and predcted output s called predcton error. LPC mnmzes the predcton error to fnd out the coeffcents. The cepstrum s the nverse transform of the log of the magntude of the spectrum. Useful for separatng convolved sgnals (le the source and flter n the speech producton model). Log operaton separates the vocal tract transfer functon and the voce source. Vocal Tract flter has slow spectral varatons and exctaton sgnal has hgh spectral varatons. Generally provdes more effcent and robust codng of speech nformaton than LPC coeffcents edtor@aeme.com

8 Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM Fgure 7 LPCC [10] The predctor coeffcents are rarely used as features, but they are transformed nto the more robust Lnear Predctve Cepstral Coeffcents (LPCC) features. The LPC are obtaned usng Levnson-Durbn recursve algorthm. Ths s nown as LPC analyss. The dfference between the actual and the predcted sample value s termed as the predcton error or resdual [11] and s gven by, s( n) e( n) s( n) s( n) p e( n) as( n ), a0 0 p 1 a s( n ) (13) 1...(14) Optmal predctor coeffcents wll mnmze ths mean square error. At mnmum value of E, E 0, 1,,... p...(15) a Dfferentatng and equatng to zero we get, = (16) a [ a a... a 1 p r ]...(17) r Where, r [ r(1) r()... r( p)]...(18) Where R s the Toepltz symmetrc autocorrelaton matrx gven by, r(0) r(1) R.. r( p 1) r(1) r(0) a r( p 1) r( p ).. r(0) Equaton can be solved for predctor coeffcents by usng Levnson s and Durbn algorthm as follows: (0) E r[0]...(19) L1 1 r[ ] a. [ ] j1 j r j...(0) ( 1) E 89 edtor@aeme.com

9 Anagha S. Bawasar and Prabhaar N. Kota Where, 1 p a j a a j ( 1) j. a 1 j (1) () E (1 ). E 1 (3) The above set of equatons s solved recursvely for =1, p. the fnal soluton s gven by ( p) am a m Where;, where, 1 m p (4) a m ' s are lnear predctve coeffcents (LPC) Cepstral Analyss. In realty, the actual predctor coeffcents are never used n recognton, snce they typcal show hgh varance. The predctor coeffcents are more effcently transformed to a robust set of parameters nown as Cepstral coeffcents Before gong to the defnton of Cepstral coeffcents, let us go through the defnton of the Cepstrum. A cepstrum s nothng but the result of tang the Fourer transform of the logarthm of the estmated spectrum of a sgnal. The three dfferent types of cepstrum are the power cepstrum, complex cepstrum and the other one s real cepstrum. Among them, the power cepstrum, n partcular, fnds applcaton n the analyss of human speech. The name cepstrum was derved from the word spectrum by reversng the frst four letters. The steps through whch the nput speech sgnal goes through are preprocessng then feature extracton and after that modelng. After preprocessng, the sgnal reduces complex complexty whle operatng on speech sgnal. In ths one partcular reduces the number of samples of operatons. It s very dffcult to wor on huge set of samples; therefore nstead of worng on such a large set of samples, we restrct our operatons to a frame of suffcently reduced length. After the sgnal condtonng or after pre-processng the speech sgnal goes through the feature extracton stage. Here the features are extracted by usng DCT. That s calculatng the coeffcents usng DCT. Mathematcally; Ceps dct log( abs( FFT( y ))) (5) wndow The prncpal advantage of Cepstral coeffcents s that they are generally decorrelated and ths allows dagonal covarances. However, one mnor problem wth them s that the hgher order Cepstral are numercally qute small and ths results n a very wde range of varances when gong from the low to hgh Cepstral coeffcents. Cepstral coeffcent can be used to separate the exctaton sgnal (whch contans the words and the ptch) and the transfer functon (whch contans the voce qualty). The cepstrum can be seen as nformaton about rate of change n the dfferent spectrum bands. The recursve relaton between the predctor coeffcents and 90 edtor@aeme.com

10 Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM Cepstral coeffcents s used to convert the LP coeffcents (LPC) nto LP Cepstral coeffcents c c ln...(6) 0 c m a m m1 1 m c a m 1 m p (7) c m m m1 c 1 a m...(8) Where the gan term n the LP analyss and d s s the number of LP Cepstral coeffcents. 4. GAUSSIAN MIXTURE MODEL (GMM) AND UNIVERSAL BACKGROUND MODEL The text ndependent speaer recognton system used n ths paper uses GMM-UBM approach for modelng purpose. Generally; two models are beng developed here, one s target speaer model and other s mpostor model (UBM). It has generalzaton ablty to handle unseen acoustc pattern [1]. In a bometrc system, GMM s commonly used as a parametrc model of probablty dstrbuton contnuous measurements or features. The features used are generally vocal tract features n any speaer dentfcaton system. As we all now that GMM are more lely used for text-ndependent speaer dentfcaton as the pror nowledge about what speaer wll say. Hence modelng s generally done n GMM.A Gaussan mxture model s a weghted sum of M component Gaussan denstes as gven by the equaton [13]. p( x ) M 1 w g( x, )...(9) Where; x s a D-dmensonal contnuous-valued data vector or features), (.e. measurement g, ( x, ) = 1.. M are the mxture weghts, and w, = 1 M s the component Gaussan denstes. Each component densty s a D-varate Gaussan functon of the form; g( x, ) exp{ ( x )' ( )}...(30) / 1/ x D ( ) Wth mean vector and covarance matrx the mxture weghts satsfy the constrant that M 1 1 The complete Gaussan mxture model s parameterzed by the mean vectors, covarance matrces and mxture weghts from all component denstes. These parameters are collectvely represented by the notaton, {,, } =1 M. (31) 91 edtor@aeme.com

11 Anagha S. Bawasar and Prabhaar N. Kota For a sequence of T tranng vectors X { x 1,... x T }.The GMM lelhood, assumng ndependence between the vectors, can be wrtten as p( X ) p( x t ) (3) t1 For utterances wth T frames, the log-lelhood of speaer models s; L ( X) log p( X ) s T s T t1 log p( x t )...(33) s For speaer dentfcaton, the value of L s (X ) s computed for all speaer models s enrolled n the system and the owner of the model that generates the hghest value s the returned as the dentfed speaer. Durng tranng phase, Feature vectors are beng traned usng Expectaton and Maxmzaton (E&M) algorthm. An teratve update of each of the parameters n, wth a consecutve ncrease n the log lelhood at each step. GMM are generally used for text-ndependent speaer dentfcaton. The drawbac of the prevous systems s beng overcome by usng GMM-UBM. It overcomes on the cost of the mode; t s not as expensve that of the GMM. There s no need for the vocabulary database or bg phoneme. GMM s more advantageous than HMM. Capturng the general characterstcs of a populaton and accordngly adaptng t to ndvdual speaer s the basc dea of UBM. In other words more brefly UBM s defned as the model whch s used n many applcaton areas but one of them s bometrc system whch s used to compare the person s ndependent feature characterstcs aganst person specfc feature model durng decson of acceptance or rejecton. UBM s also sad as GMM only wth large set of speaers. The UBM s traned wth the EM algorthm on ts tranng data. For the speaer recognton process, t fulflls two man roles: It s the apror model for all target speaers when applyng Bayesan adaptaton to derve speaer models and t helps to compute log-lelhood rato much faster by selectng the best Gaussan for each frame on whch lelhood s relevant. Ths wor proposes to use the UBM as a gude to dscrmnatve tranng of speaers [14]. 5. COMPARATIVE RESULTS OF FUSED MEL FEATURE SETS AND LINEAR PREDICTIVE CEPSTRAL COEFFICIENTS The man method focus n ths paper s fuson of the both algorthms that are used both MFCC and IMFCC respectvely. The man am s to compare the fused results to the results obtaned from LPCC. That s here the accuracy obtaned from the fused Mel feature set s compared wth the Lnear predctve Cepstral coeffcent. The better results among them wll gve us the accurately dentfed speaer respectvely among the database used for t. The system performs better f the two or more combnaton of them were suppled wth nformaton that s complementary n nature. For obtanng the dentfcaton accuracy MFCC and IMFCC features whch are complementary to each other can be fused together. There are many possble ways for combnng such as; product, sum, mnmum, maxmum, medan, average etc, can be used. The sum rule outperforms as compared to the other combnatons and s most reslent to estmaton errors. Let us go through the bloc dagram of Fused Mel feature set along wth LPCC wth GMM-UBM modelng technque. 9 edtor@aeme.com

12 Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM From fgure 7 and 8 we can say that system ncludes tranng and testng for fused Mel feature Set and LPCC feature set. The mplementaton s done on TIMIT database. TIMIT corpus s one of the standard databases used by the many researchers for the purpose of speaer dentfcaton. Ths paper also concentrates on the TIMIT database. It comprses of the 16 speaers. Fgure 8 Steps nvolved n speaer dentfcaton system (fused Mel features sets) [5] Fgure 9 Speaer dentfcaton system (LPCC) [6] The recordngs are from 8 dalect regons. Each speaer has 10 utterances respectvely Total 160 sentences recordngs (10 recordngs per speaer). The audo format s.wav format, sngle channel, 16 Hz samplng, 16-bt sample, PCM encodng. The features are beng extracted by usng Gaussan Mel scale flter ban. The feature vectors are traned by usng Expectaton Maxmum algorthm. From the dagram, we can say that separate model s beng created for each speaer [5]. Features are extracted from the ncomng test sgnal and then the lelhood of these features wth each of the speaer model s determned. These are ncluded n the testng step. The lelhood for MFCC and IMFCC as well as for LPCC s determned. We have drawn two separate bloc dagrams for fused Mel feature sets and LPCC. In frst dagram a unform weghted sum rule s adopted to fuse the scores from the two classfers. S com ws MFCC ( 1 w) S IMFCC (34) 93 edtor@aeme.com

13 s No. of Mxtures Anagha S. Bawasar and Prabhaar N. Kota com s combned score of MFCC and IMFCC, and SMFCC, S are scores generated by the MFCC model and scores generated by IMFCC Model and w IMFCC s fuson coeffcent. On smlar Lne, we calculated the values for LPCC and are denoted by S. LPCC The accuracy for Fuson and LPCC are calculated and are compared. The usage of weghts and number of mxtures can be changed to dfferent values to test the system for optmum result. Table I shows the performance level of proposed system for dfferent weghts and mxtures. As stated we are usng standard database TIMIT of 16 speaers, we need to dvde these nto two for tranng and testng purpose. For ths purpose, the UBM consst of 5 speaers and GMM 11 speaers respectvely. The bacground model s generated by UBM. The value of alpha that s flterng constant s ept as 0.97 respectvely. The accuracy s beng calculated on the bass of False postve and False negatve. In false postve a false speaer s accepted as true one. Whle n False Negatve, a true speaer s rejected as an mpostor. The formula for accuracy calculaton s: Accuracy n percentage=100-((fp+fn)*100/ (M*N)) Where, M*N= sze of the confuson matrx. Table I. Comparatve Results for dfferent number of Mxtures and weghts for gven proposed system Score threshold=0.6 Fuson (%) LPCC (%) Score threshold=0.77 Fuson LPCC (%) (%) Score threshold=0.8 Fuson LPCC (%) (%) Score threshold=0.97 Fuson (%) LPCC (%) Fgure 10 Graphcal Representaton of table 1 From above table, we can see that the varous accuracy percentages we got for the dfferent values of the mxtures and the dfferent values of score threshold. The value of threshold ncreases the accuracy s ncreasng accordngly. But n all the accuracy for the Fuson s good as compared to the LPCC. The performance of the fused system exceeds the performance of LPCC. The percentage of maxmum performance s 95.04% and hence lewse we have found out good dentfcaton wth lmted errors edtor@aeme.com

14 Comparatve Study of LPCC and Fused Mel Feature Sets For Speaer Identfcaton Usng GMM-UBM 6. CONCLUSION Many methods were used earler for feature extracton. They nclude MFCC, IMFCC, etc. These two algorthms wored ndvdually well and gve good accuracy. Though the IMFCC help MFCC to mprove ts accuracy further, these two algorthms are combned together and s called as fused Mel Feature set. In ths, the Gaussan Mxture Model s beng evaluated for speaer dentfcaton. The performance s ncreased by fusng the complementary nformaton. As shown n table, the accuracy has been calculated for LPCC and Fuson and s seen 95.04% at weght 0.77 and 0.8 respectvely for Fuson whch s better than and at weght 0.77 and 0.8 for LPCC. The more enhancements may be done by changng the modelng technque and by changng varous combnatons of weghts. The future scope may nclude an applcaton of same database approach try to develop a real-tme applcaton and also the system can be developed by usng artfcal neural networ based approach. REFERENCES [1] J. Kttler, M. Hatef, R. Dun, J. Mataz, On Combnng Classfer, IEEE Transacton, Pattern Analyss and Machne Intellgence, 0(3), pp.6-39,march [] Rana, Muesh, and Salon Mglan, Performance analyss of MFCC and LPCC Technques n Automatc speech Recognton, Internatonal Journal of Engneerng and Computer Scence, 3(8), pp , August, 014 [3] Srdharan, Srdha & Wong, Edde, Comparson of Lnear Predcton Cepstrum Coeffcents and Mel-Frequency Cepstrum Coeffcents for language dentfcaton, Proceedngs of Internatonal Symposum on Intellgent Multmeda, Vdeo and Speech Processng, pp , -4 May 001 [4] Charoborty Sandpan, and Goutam Saha, Improved text-ndependent speaer dentfcaton usng fused MFCC & IMFCC feature sets based on Gaussan flter, Internatonal Journal of Sgnal Processng, 5(1), pp , 009 [5] R. Shantha Selva Kumar, S. selva Ndhyananthan, Anand, Fused Mel Feature sets based Text-Independent Speaer Identfcaton usng Gaussan Mxture Model, Internatonal Conference on Communcaton Technology and System Desgn, Proceda Engneerng, 30, pp , 01 [6] Anagha S. Bawasar, Prabhaar N. Kota, Speaer Identfcaton Based on MFCC and IMFCC Usng GMM-UBM, Internatonal Organzaton of Scentfc Research (IOSR Journals), 5(), pp , March-Aprl 015 [7] Cheng, Octavan, Waleed Abdulla, and Zoran Salcc, Performance evaluaton of front-end processng for speech recognton systems, School of Engneerng Report. The Unversty of Aucland, Electrcal and Computer Engneerng, 005 [8] Rabner, L. and Juang, B, Fundamentals of speech recognton, Prentce Hall, Inc., Upper Saddle Rver, New Jersey, Aprl 1993 [9] Rabner, L.R., Schafer, R.W., Dgtal Processng of Speech Sgnals, Prentce Hall, [10] Pallav P. Ingale and Dr. S.L. Nalbalwar, Novel Approach To Text Independent Speaer Identfcaton, Internatonal Journal of Electroncs and Communcaton Engneerng & Technology, 3(), 01, pp [11] Chang, Wen-Wen, Tme Frequency Analyss and Wavelet Transform Tutoral Tme-Frequency Analyss for Voceprnt (Speaer) Recognton, Natonal Tawan Unversty edtor@aeme.com

15 Anagha S. Bawasar and Prabhaar N. Kota [1] Pazhanrajan, S., and P. Dhanalashm, EEG Sgnal Classfcaton usng Lnear Predctve Cepstral Coeffcent Features, Internatonal Journal of Computer Applcatons, 73(1), pp., 013 [13] Chao, Y-Hsang; Tsa, W.-H.; Hsn-Mn Wang, Dscrmnatve Feedbac Adaptaton for GMM-UBM Speaer Verfcaton, Chnese Spoen language Processng( ISCSL) 6th Internatonal Symposum on, pp.1,4, Dec. 008 [14] Manan Vyas, A Gaussan Mxture Model Based Speech Recognton System Usng Matlab, Sgnal & Image Processng: An Internatonal Journal (SIPIJ), 4(4), August 013 [15] Amr Rashed, Fast Algorthm For Nosy Speaer Recognton Usng Ann, Internatonal journal of Computer Engneerng & Technology, 5(), 014, pp [16] Vplav Gautam, Saurabh Sharma,Swapnl Gautam and Gaurav Sharma, Identfcaton and Verfcaton of Speaer Usng Mel Frequency Cepstral Coeffcent, Internatonal Journal of Electroncs and Communcaton Engneerng & Technology, 3(), 01, pp [17] Scheffer N, Bonastre. J.F, UBM-GMM Drven Dscrmnatve Approach for Speaer verfcaton, Speaer and Language Recognton worshop, IEEE Odyssey, pp.1-7, 8-30 June edtor@aeme.com