Phone Recognition on TIMIT Database Using Mel-Frequency Cepstral Coefficients and Support Vector Machine Classifier

Transcription

1 IJISET - Inernaonal Journal of Innovave Scence, Engneerng & Technology, Vol. Issue, January Phone Recognon on TIMIT Daabase Usng Mel-Frequency Cepsral Coeffcens and Suppor Vecor Machne Classfer Seyed Hamd Paylakh, Dr. Jall Shraz Elecrcal Engneerng Deparmen, Khavaran Insuon for Hgher educaon, Mashad, Iran Asssan Professor, Elecrcal Engneerng Deparmen, Islamc Azad Unversy, Gonabad branch, Gonabad, Iran Absrac In he presen arcle he speech recognon s performed on 6 Englsh phones exraced from TIMIT daabase. For hs reason he speech sgnal of each phone s dvded no 0 mllsecond frames and for each frame, he MFCC coeffcens, her frs and second dervaves, energy of he frame, as well as s frs dervave were exraced as 4 feaures of he vecor frame and by he use of he suppor vecor machnes, he frame classfcaon no Englsh phonecs s performed. In order o mprove he resuls he snusodal fer and Independen Componen Analyss (ICA) are appled on feaure vecors. Accordng o he resuls of he sudy, here have been 4.93% error rae for he es daa whch presens a sgnfcan mprovemen compared wh he recen researches n hs area and demonsrae he hgh performance of he feaures and algorhms used n he arcle. Keywords: speech recognon, Mel-frequency Cepsral coeffcens, suppor vecor machnes, TIMIT. Inroducon In he las four decades, Auomac Speech Recognon (ASR) sysem has become a specfc research goal and sgnfcan resuls have been acheved. However, research n hs area, sll connues hopng o acheve near-zero error rae. On he oher hand he performance of large vocabulary auomac speech recognon sysems (VASR) depends on he dagnosc qualy of he phone recognzers. Ths has allowed he research eams o develop and mprove he phone recognzers as far as possble n order o acheve he opmum performance. Also phone recognon sysems n addon o VASR can be used n a wde range of applcaons such as screenng keyword, language recognon, speaker denfcaon, and recognon and applcaons for musc and ranslaons. Snce creang robus acousc models by he use of eachng algorhms o well sued daa s possble, hs arcle works on phone recognon on TIMIT daabase and for hs purpose he Mel-frequency Cepsral coeffcens (MFCC) and her frs and second dervaves as well as energy coeffcen frame and s frs dervave as he feaures coeffcens and suppor vecor machnes are used o classfy hese feaure coeffcens o Englsh phones. In he second secon of hs arcle he researches n phone recognon and her resuls are ced. In he hrd par of hs arcle he daabases beng used and he appled echnques are represened. Then n secon 4 he praccal ess and resuls are descrbed and secon fve s he concluson of he presen sudy.. The Researches n Phone Recognon Much work has been n he feld of phone recognon due o s wde applcaons. The summary of he mos recen works on TIMIT daabase and he resuls are gven n Table. Year Table : Some of he works done on TIMIT and her resuls Auhors Sha and Saul. [] Scanlon [] Hfny and Renals [3] Prabhavalka r [4] ee and shades [5] The used feaure coeffcens MFCC, ΔMFCC PP, ΔPP, ΔΔPP - S p f poseror MFCC, CDBN Speech recognon echnology GMMs raned as SVMs MP/HMM Augmened CRFs M SVM Phone Error rae (%)

2 IJISET - Inernaonal Journal of Innovave Scence, Engneerng & Technology, Vol. Issue, January Daabase and he Necessary Theores 3.. Paern Recognon 3.. TIMIT Daabase The connuous Corpus of TIMIT [6] acousc-phonec speech s made of Englsh speech whch s recorded usng a mcrophone a a rae of 6 khz and 6 b resoluon. Ths daabase ncludes 6300 senences (5.4 hours) communcaed by 630 speakers from 8 regonal dalecs of he Uned Saes. Each speaker arculaed 0 senences and all senences are manually labeled based on he phone level. The man verson of TIMIT s based on 6 phonecs shown n Table. Table : 6 phones of TIMIT orgnal phones y h eh ey ae aa aw ay ah ao oy ow uh uw ux er ax x axr ax-h l r w y hh hv el m n nj em en eng nx s sh z zh f h v dh jh ch b D G P T K Dx Q Pau Ep Bcl Dcl Gcl Pcl Tcl Kcl One of he man reasons ha hs speech frame has become a sandard for he of speech recognon communy s ha each senence s labeled manually based on he phonec level, and specfed by allocaon of he codes for he speakers number, gender and regonal accen. h# There are dfferen approaches o speech recognon. One of he mos successful approaches s he recognon-based approach ha almos all recen successful sysems operae based on. In hs approach he speech s modeled based on some phonec srng (for example, word, syllable, hree-phoneme or phoneme) and for he recognon he ex of he speech s esmaed by denfyng hese uns and juxaposng hem ogeher. Speech recognon sysems, whch use hs approach, have wo phases of ranng and esng. In he ranng phase, he paerns of each class whch are he very phonec uns are modeled usng some mehods. The comparson of he npu speech wh he raned paerns s performed n he es phase n order o recognze he phonec uns n he speech. A speech recognon sysem consss of wo man componens of feaure exracon and modelng un (ranng phase), and he use of models or search (for es phase and use). In hs srucure, each of he relaed uns can be performed n several ways Feaure Exracon The exracon un, also known as preprocessng, s one of he necessary uns n paern recognon applcaons. The goals of hs un n he speech recognon sysem are speech recognon, reducng he amoun of compuaon, and removng he redundances n he speech sgnal by exracng a lmed number of s parameers. Parameers exraced by hs un should be relaed o he nended applcaon. In oher words, for he applcaons of speaker s ndependen speech recognon, he parameers wh leas sensvy o he phonecs of a speech wh respec o he uerance and he speaker should be exraced. However, for speaker dependan applcaons such as denfcaon of he speaker s beer for he exracon un o exrac speaker-dependen parameers, such as one dependen feaures, shape and lengh of he audo rack, sep duraon and ec. Snce all subsequen operaons work on hs feaure, ulzaon of a powerful mehod s one of he key facors of a recognon sysem. By usng he mehods of feaure exracon, he sgnal s ransferred no he parameers called feaure vecor, and he classfcaon s done on hese parameers. There are several mehods for exracng feaures some of whch benef from speech producon n human audo sysem dea and he res benef from he audory sysem. There are dfferen mehods for feaure exracon among whch he Percepual near Predcve (PP) and he Melfrequency Cepsral coeffcens (MFCC) are more successful and effecve han he res of hem. In hs 59

3 IJISET - Inernaonal Journal of Innovave Scence, Engneerng & Technology, Vol. Issue, January arcle he MFCC mehod explaned n he nex secon s used MFCC Where S[m] logarhm s he m _ h oupu of fler, M s he oal number of flers and C[n] s equal o he n _ h MFCC. The esmaon of MFCC s based on human audory sysem for an audo sgnal [7]. One of he reasons of he hgh performance of he coeffcens s he hgh resoluon. Tha s o say, small changes whch are he resuls of hs scale show her hgh effecveness. Anoher srengh pon of hs mehod s ha besdes elmnang he specral srucure, and summarzng he daa, reduces he correlaon beween he properes and mproves he classfcaon. The mehodology for calculang he coeffcens s explaned below MFCC Calculaon Each frame of he audo sgnal s mulpled by a Hammng wndow and hen he Dscree Fourer ransform s aken from he resulng sgnal. The sze of he Fourer ransform has been calculaed and he followng seps are performed on he obaned envelope specrum: A: The sgnal specrum s passed hrough 40 flers wh Mel-scale bandwdh. These flers smulae he human audory percepon frequency separaon. The prmary3 flers wh cener frequency changes are lnear (The frequency dsance beween he ceners s 33/33 khz) and he nex 7 flers are logarhmc wh cener frequency changes ( he cenral frequency of he fler s greaer han he cenral frequency of he prevous fler). Flers are rangular and each fler sars from he cenral frequency of he prevous fler and ends n he cener of he nex cenral frequency and s maxmum s whn s own cenral frequency. B: The logarhm of oupu flers s obaned. C: In order o reduce he numbers of componens of he feaure vecor he logarhmc values of he 40 oupu flers are mulpled by he dscree cosne ransform and he obaned oupu equals he number of MFCC coeffcens. Eq. () shows dscree cosne ransform on he flers oupu: C[ n] M m0 n( m S[ m]cos( M ) ) () Dela Cepsral Coeffcens Cepsral coeffcens express he feaures of speech sgnal and have a grea benef o enhance he accuracy of speech recognon. In order o maxmze he accuracy of he sysem s possble o use he dervaons of hese coeffcens based on me. In fac he Cepsral coeffcens model he sac nformaon of speech sgnal and hey are sensve o he saus of dalecs and her changes whle he dervaves of he Cepsral coeffcens conan dynamc nformaon ransfer daa beween dfferen saes of dalecs. The negraon of Cepsral coeffcens and her dervaves can presen beer feaure of he speech sgnal. The Cepsral coeffcens dervaon or he Dela Cepsral can be obaned from he Eq. (): j C j. C () 4 j _ h MFCC coeffcen exraced from Where C s he he _ h frame of each phone. To calculae he second dervaves of he coeffcens or he Dla- Dela Cepsral coeffcens, he value of he Cepsral coeffcen mus be placed n equaon fer The purpose of exracon of sgnal feaure coeffcens s o use hem n specal analyss or applcaon such as recognon. Hence all he measures and soluons wll be appled n order o mprove he recognon sysem. Classfed lferng s one of he ways of mprovng he operaons [8]. In hs mehod, n an applcaon such as speech recognon, when Cepsral coeffcens (MFCC or PCC) are used generally he lower coeffcens are sensve o slope of he range and he hgher coeffcens are sensve o nose(he nose can be caused by such hngs as he calculaon error or pluralzaon). In order o reduce he menoned sensves he Cepsral vecor s mulpled by a wegh vecor accordng o equaon (3). Cˆ ( n) C( n). W ( n) (3) Where C (n) he facors of he feaure vecor, W represens fer vecor funcon and C (n) presens he fer vecor. The lfers have varous ypes, ncludng 530

4 IJISET - Inernaonal Journal of Innovave Scence, Engneerng & Technology, Vol. Issue, January lnear, exponenal and snusodal and he snusodal ype s used n hs arcle and he relaed relaon s as follows: W sn( ),,,..., (4) Where represens he Cepsral feaures coeffcens whch equals Suppor Vecor Machne Suppor vecor machne s a bnary classfer. One of he advanages of hs machne s he separaon of he classes accordng o her dsrbuon. Ths echnque s an opmal separaor ha has shown s effcency a classfyng daa n dfferen applcaons [9] near SVM Ths ype of classfer separaes wo classes wh a border lne. In he ranng phase, usng all he ranng vecors and an opmzaon algorhm, a number of ranng samples separang he boundares of he classes are obaned and hese ranng samples are called he suppor vecors. In order o analyze he suppor vecor machne s assumed ha he daa are formed of wo separae classes and he number of ranng vecors x s. And he wo classes are labeled y and y. In general, he separaon boundary lne s acheved by equaon (5): wx b 0 (5) Where x s a pon on he separang lne and w s a perpendcular vecor he separaon boundary. Thus f x s no a suppor vecor, y.( w. x b). The boundary lne of he wo separae classes s presened n Fg. Gven he choce of he values of b and w, here are many dvdng lnes ha separae he wo classes wh zero error. To oban he opmal separaon boundary, he neares ranng samples of wo classes are obaned and hen he dsance from each oher are calculaed n a drecon perpendcular o he boundares ha separae he classes. The Boundary separaon beween he wo classes ha has he maxmum margn s he opmal separaon and he Eq (6) s obaned. y ( w. x b),,...,, mn w w. b To solve he opmzaon problem he ndefne agrange coeffcen can be used. In hs mehod he opmzaon problem reaches he Eq (7) n whch he s he agrange coeffcen. max [ y ( x. x ) y j j j,..., j ],..., 0 (7) y 0 By solvng he above equaon he value of w equals w (6) y x and s greaer han zero for suppor vecors and zero for non-suppor vecors. Then afer fndng w usng Eq (8) for each suppor vecor he b value s calculaed and he fnal b wll be obaned by averagng he oal b. [ y ( w. x b) ] 0,,..., The fnal separaon s obaned by Eq (9). ( x) sgn( w. x b) (8) (9) Nonlnear SVM Fg : he boundary lne separang he wo classes [0] The lnear boundary n wo separae classes can be obaned by lnear SVM, bu when he wo classes are nseparable, he separaon of classes wh he boundary lne s assocaed wh error. In hs case, frs he feaure vecors of dfferen classes are ransferred no a space wh hgher dmensons hrough a non-lnear ransformaon Φ, and hen wh a lnear funcon or a hyper page he separaons are made n he new spaces. In 53

5 IJISET - Inernaonal Journal of Innovave Scence, Engneerng & Technology, Vol. Issue, January he new space usng he prevous equaons and replacemen of x wh ( x ) and consderng an error value for each vecor he opmal separaon boundary s calculaed. Opmzaon problem o fnd he opmal separaon boundary reaches Eq (0) max [ y ( ( x ). ( x )) y j j j,..., j,..., c 0 (0) y 0 In he above equaon c s a consan value ha specfes he error value he greaer he value he greaer he value of he error wll be. In he above relaon nsead of usng a core funcon accordng o Eq () s used. k x, x ) ( x ) ( x ) () ( j j So nsead of he core funcon s placed and he opmzaon problem can be solved. Core funcons are symmerc posve and defne. There are hree mporan funcon ha can be used ncludng: A) polynomal funcon: d k ( x, ( x. y ) where d s he degree of he polynomal. B) GRBF : x y k( x, exp( ) Where s a Gaussan funcon wdh C) Sgmod funcon: k ( x, anh( k( x. ) Where k and are he Scale and nercep parameers In hs paper of he ype B (GRBF) s used Independen Componen analyss Independen componen analyss s a sgnal processng echnque for blnd source separaon, he feaure exracon and sgnal deecon. In hs sudy we use he ndependen componen analyss o elmnae he Generalzed Radal Bass Funcons j dependence of he componens of he feaure vecors and as he name of hs analyss suggess he man purpose of hs converson s o fnd a represenaon so ha he componens of he feaure vecors are ndependen from each oher. Ths s done by makng all feaure vecors covarance marx enres zero excep he man dagonal enres []. 4. Praccal Resuls In hs paper frs he phonecs of he TIMIT daabase sampled a a rae of 6 KHz are exraced and hen sequencng he smlar phonecs 60 phonec classes were obaned. The followng seps are performed on he dgal sgnal. Pre emphass: snce he exced voced phone of he larynx has - db / oc slope and he radaon converson funcon s n a way ha he frequency response has ncreasng slope +6 db / oc, n order o compleely elmnae he effecs of excaon and he effec of lps radaon he loadng fler s performed by +6 db / oc characersc. Ths fler s known as Preemphass. If ) s he npu speech sgnal, hus: x(n y ( n) x( n) ax( n ), 0.9 a (5) y(n Where ) s he Pre-emphaszed sgnal. In hs paper a equals Wh he Pre-emphass he sgnal creaed by nense nose s elmnaed and becomes a unform sgnal. Wndowng: Due o he characerscs of he speech sgnal over me and s nsably feaure, he exracon from a relavely large me nerval does no provde relable nformaon. Bu snce he human speech organs canno change suddenly, s possble o assume sable n 0-40 mllsecond nervals and he speech sgnals are saonary. Tha's why he speech sgnal s dvded no hese slos. To do hs, he sgnal lengh s mulpled by 0 ms wndows and snce wndows wh sharp edges n he frequency doman nclude all frequences he wndowsll should be gradually reduced o he lowes mpac on he sgnal's frequency. In hs paper, he Hammng wndow s used whch s Gaussan and has 50% overlap. Nex, from each of he wndows3 MFCC coeffcens s exraced Accordng o wha was sad n Secon 3.3. Then he frs and second dervaves of he coeffcens were obaned and n addon, he energy characerscs of he frame and s frs dervave ha are appled coeffcens n he feld of speech recognon are calculaed by he Eq (6) and (7) and added o he feaure vecor and fnally each feaure vecor conans a 4-dg coeffcen feaure. 53

6 IJISET - Inernaonal Journal of Innovave Scence, Engneerng & Technology, Vol. Issue, January E E N n a ( n) (6) j j. E j 4 j (7) Number of ess MFCC ΔMFCC ΔΔMFCC _ Where he s he sample of he E s he frame energy of he _ h _ h frame, a frame and N s he lengh of he frame. In hs paper he lengh of each frame s equal o 30 samples (0 ms) and each frame has consdered as 0 ms overlap wh he prevous frame. The las sep before classfcaon n order o equalze he value of he daa and preven he decrease sysem speed and accuracy of daa) are normalzed by Eq (8) x x x mn (0,) (8) xmax xmn In whch x max s he end and x mn s he begnnng of he nerval and x (0,) s he normalzed value n he nerval (0, ). Fnally he classfcaon s performed by ranng suppor vecor machne based on he normalzed feaure vecors. The classfcaon s summarzed n Table 3. As he able 3 suggess addng he coeffcens of he dela and MFCC dela and energy coeffcen and energy- Dela mproved he resuls sgnfcanly. Table 3: Resuls of dfferen feaure vecors E ΔE The error rae for ran daa (%) The error rae for es daa (%) _ In addon o addng useful feaures o he feaure vecors, one of he mos useful applcaons of hs work s o apply ndependen componen analyss on he feaure vecors. As shown n Fg, he resuls of each expermen n able 3 have sgnfcan error reducon afer he ndependen componen analyss compared wh he resuls before. Fg : The resuls of ess conduced before and afer ICA 533

7 5. Concluson IJISET - Inernaonal Journal of Innovave Scence, Engneerng & Technology, Vol. Issue, January In hs paper, n order o classfy he phonecs of Englsh, speech sgnal of each sound s dvded no 0 mllsecond wh 0 mllsecond overlap. By usng he approprae feaure vecors and ICA algorhm, a very good resul wh 4.93% error rae s obaned n classfyng he es frames. These feaure vecors conan 3 MFCC, 3 MFCC, 3 MFCC coeffcen frame energy coeffcen and derved energy coeffcen. Snce he bes resul n oher works conans 8.5% error he obaned resuls show sgnfcan mprovemen. The use of genec algorhms o selec he bes feaures, n order o ncrease he classfcaon accuracy s desred. References [] F. Sha, and K Saul, "arge margn Gaussan mxure modelng for Phonec classfcaon and recognon," n Acouscs, Speech and Sgnal Processng, 006. Proceedngs Icassp wo housand and sx. 006 IEEE Inernaonal Conference on, 006, pp. II. [] P. Scanlon, DPW Ells, and RB Relly, "Usng expers for mproved speech recognon broad Phonec group," Audo, Speech, and anguage Processng, IEEE Transacons on, vol. 5, pp , 007. [3] Y. Hfny, and S. Renals, "Speech recognon usng augmened Condonal random felds," Audo, Speech, and anguage Processng, IEEE Transacons on, vol. 7, pp , 009. [4] R. Prabhavalkar, TN Sanah, D. Nahamoo, B. Ramabhadran, and D. Kanevsky, "An evaluaon of poseror modelng echnques for Phonec recognon," n Acouscs, Speech and Sgnal Processng (Icassp), he,03h IEEE Inernaonal Conference on, n 03, pp [5] H. ee, P. Pham, Y. argman, and A. Y. Ng, "Unsupervsed learnng feaure for audo classfcaon usng Convoluonal deep belef neworks," n Advances n neural nformaon processng sysems,,009, pp [6] J. S. Garofolo,. F. amel, W. M. Fsher, J. G. Fscus, and D. S. Palle, "DARPA CD-ROM Tm acousc- Phonec Connous speech corpus. NIST speech dsc o.," NASA STI / Recon Techncal Repor N, vol. 93, pp. 7403, 993. [7] S. Davs, and P. Mermelsen, "Comparson of paramerc Represenaons for Monosyllabc word recognon n connuously spoken Senences," Acouscs, Speech and Sgnal Processng, IEEE Transacons on, vol. 8, pp , 980. [8] K. K. Palwal, " On he use of fler-bank energes as feaures for robus speech recognon," n Sgnal Processng and Is Applcaons, 999. Isspa'99. Proceedngs of he Ffh Inernaonal Symposum on, he 999h, pp [9] C. Cores and V. Vapnk, "Suppor-vecor neworks," Machne learnng, vol. 0, pp , 995. [0] D. Meyer and F. T. Wen, "Suppor vecor machnes," The Inerface o bsvm n package E07, January, vol. 0, 04. [] A. Hyvarnen, J. Karhunen, and E. Oja, "Independen componen analyss" vol. 46: John Wley and Sons,