Development of the Feature Extractor for Speech Recognition

ENEKO AÑORGA DEVELOPMENT OF THE FEATURE EXTRACTOR FOR SPEECH RECOGNITION DIPLOMA WORK MARIBOR, OCTOBER 2009 i FAKULTETA ZA ELEKTROTEHNIKO, RAČUNALNIŠTVO IN INFORMATIKO 2000 Ma ibo , Sme ano a ul. 17 Diploma Wo k o Elec onic Enginee ing S uden P og am DEVELOPMENT OF THE FEATURE EXTRACTOR FOR SPEECH RECOGNITION S uden : Eneko A ñ o ga S uden p og am: Elec onic Enginee ing Men o : P o . D . Riko ŠAFARIČ Asis . P o . D . Suzana URAN Ma ibo , Oc obe 2009 ii ACKNOWLEDGMENTS Thanks o P o . D . Riko ŠAFARIČ o his assis ance and help ul ad ices in ca ying ou he diploma wo k. Special hanks o my amily and iends who a e in all momen s beside me. iii DEVELOPMENT OF THE FEATURE EXTRACTOR FOR SPEECH RECOGNITION Key wo ds: oice ope a ed wheelchai , speech ecogni ion, oice ac i i y de ec ion, neu al ne wo ks, ul asound senso ne UDK: 004.934:681.5(043.2) Abs ac Wi h his diploma wo k we ha e a emp ed o gi e con inui y o he p e ious wo k done by o he esea che s called, Voice Ope a ing In elligen Wheelchai – VOIC [1]. A de elopmen o a wheelchai con olled by oice is p esen ed in his wo k and is designed o physically disabled people, who canno con ol hei mo emen s. This wo k desc ibes basic componen s o speech ecogni ion and wheelchai con ol sys em. Going o he g ain, a speech ecognize sys em is comp ised o wo dis inc blocks, a Fea u e Ex ac o and a Recognize . The p esen wo k is a ge ed a he ealiza ion o an adequa e Fea u e Ex ac o block which uses a s anda d LPC Ceps um code , which ansla es he incoming speech in o a ajec o y in he LPC Ceps um ea u e space, ollowed by a Sel O ganizing Map, which classi ies he ou come o he code in o de o p oduce op imal ajec o y ep esen a ions o wo ds in educed dimension ea u e spaces. Expe imen al esul s indica e ha ajec o ies on such educed dimension spaces can p o ide eliable ep esen a ions o spoken wo ds. The Recognize block is le o u u e esea che s. The main con ibu ions o his wo k ha e been he esea ch and app oach o a new echnology o de elopmen issues and he ealiza ion o applica ions like a oice eco de and playe and a comple e Fea u e Ex ac o sys em. i RAZVOJ PREVODNIKA SIGNALA ZA PREPOZNAVO GOVORA Ključne besede: glaso no oden in alidski oziček, p epozna a go o a, zazna a glaso ne ak i nos i ne onske m eže, ul az očna senzo ska m eža UDK: 004.934:681.5(043.2) Po ze ek S em diplomskim delom sem poskusil nadalje a i delo aziska e z naslo om Voice Ope a ing In elligen Wheelchai – VOIC [1]. V em diplomskem delu je udi p eds a ljen az oj glaso no odenega in alidskega ozička, na ejenega za elesno p izade e ljudi, ki ne mo ejo nadzo o a i s ojih gibo . To delo opisuje osno ne komponen e go o nega nadzo a in sis ema odenja in alidskega ozička. Sis em go o nega nadzo a u a na a a d a azlična dela; p e odnik signala in p epozna alec. V em diplomskem delu se os edo očam na p e odnos us eznega p e odnika signala na osno i s anda dnega LPC Ceps um kode ja, ki pos eduje p ihajajoči go o po LPC Ceps um p os o a, emu pos opku pa sledi .i. “samoo ganizacijska ka a” (Sel O ganizing Map), ki az s i ezul a kode ja za op imalni p ikaz besed na zmanjšanih dimenzijah p os o a. Poskusni ezul a i kažejo, da lahko e po i na zmanjšanih dimenzijah p os o a zago o ijo zaneslji p ikaz izgo o jenih besed. P epozna alec je lahko p edme azisko anja š uden o udi p ihodnos i. Gla ni namen ega diplomskega dela s a bili aziska a in poskus upo abe no e ehnologije az ojne namene, kako udi upo aba aplikacij ko so snemalec in p ed ajalnik z oka e celo en sis em p e odnika signala. Table o Con en s 1 INTRODUCTION ........................................................................................................ 1 1.1 MOTIVATION ..................................................................................................... 1 1.2 OBJECTIVES AND CONTRIBUTION OF THIS WORK.................................. 3 1.3 ORGANIZATION OF THIS WORK ................................................................... 5 1.4 RESOURCES ........................................................................................................ 5 2 DEVELOPMENT ......................................................................................................... 6 2.1 BRIEF DESCRIPTION OF COLIBRI MODULE ............................................... 6 2.1.1 Ha dwa e ......................................................................................................... 6 2.1.2 So wa e ........................................................................................................ 10 2.2 DESIGN OF THE FEATURE EXTRACTOR ................................................... 11 2.2.1 Speech coding ............................................................................................... 12 2.2.1.1 Speech sampling ..................................................................................... 12 2.2.1.2 P e-emphasis il e .................................................................................. 13 2.2.1.3 Wo d Isola ion ........................................................................................ 13 2.2.1.4 Speech coding ........................................................................................ 14 2.2.1.5 Summing up ........................................................................................... 18 2.2.2 Dimensionali y educ ion using SOM ........................................................... 19 2.2.2.1 Op ional Signal Scaling .......................................................................... 22 2.2.2.2 Summing up ........................................................................................... 22 2.3 SOFTWARE DEVELOPMENT ......................................................................... 24 2.3.1 Audio Reco de and Playe ........................................................................... 24 2.3.2 Fea u e Ex ac o ........................................................................................... 27 3 CONCLUSIONS ........................................................................................................ 33 3.1 SUMMARY OF RESULTS ................................................................................ 33 3.2 DIRECTIONS FOR FUTURE RESEARCH ...................................................... 34 4 REFERENCES ........................................................................................................... 36 i GLOSARY OF SIMBOLS Name Desc ip ion 1/Â(z) a i w lp (n) H h1 (z) s(n) s'(n) (k) w lag (n) '(k) k i s 0 LP syn hesis il e LP coe icien s (a 0 = 1.0) LP analysis window Inpu high-pass il e P ep ocessed/ il e ed speech signal Windowed speech signal Au o-co ela ion coe icien s Co ela ion lag window Modi ied au o-co ela ion coe icien s Re lec ion coe icien s Sampling equency Bandwid h expansion Table 1 – Glossa y o symbols ii GLOSSARY OF ACRONYMS Ac onym Desc ip ion VOIC DSP LPC LP SOM CE SODIMM CAN GPIO BSP ITU ITU-T FE VAD DTX CNG RNN VQ HMM Voice Ope a ed In elligen Wheelchai Digi al Signal P ocesso Linea P edic ion Coding Linea P edic ion Sel O ganizing Maps Compac Edi ion Small Ou line Dual In-line Memo y Module Con ol A ea Ne wo k Gene al Pu pose Inpu /Ou pu Boa d Suppo Package In e na ional Telecommunica ion Union Telecommunica ion S anda diza ion Sec o Fea u e Ex ac o Voice Ac i i y De ec ion Discon inuous T ansmission Com o Noise Gene a o Recu en Neu al Ne wo k Vec o Quan iza ion Hidden Ma ko Model Table 2 – Glossa y o ac onyms De elopmen o he Fea u e Ex ac o o Speech Recogni ion 7 Figu e 2.1 – Colib i PXA320 Module Speci ica ions: CPU PXA320 806MHz Memo y 128MB DDR RAM (32Bi ) 1GB NAND Flash (8Bi ) In e aces 16Bi Ex e nal BUS Compac Flash/PCMCIA LCD (SVGA) Touch Sc een Audio I/O (16Bi S e eo) CMOS image senso I2C SPI 2x SD Ca d USB Hos /De ice 100MBi E he ne 2x UART I DA PWM 127 GPIOs So wa e P e-ins alled Windows CE 5.0/6.0 Size 67.6 x 36.7 x 5.2 mm Tempe a u e Range 0 o +70°C -45 o +85°C (IT e sion) 8 Eneko Año ga, Diploma Wo k In o de o ha e a lexible de elopmen en i onmen o explo e he unc ionali y and pe o mance o he Colib i modules he Colib i E alua ion Boa d is used (See Figu e 2.2). Besides he use in e aces i p o ides nume ous communica ion channels as well as a con igu able jumpe a ea o hook up he Colib i GPIOs o he desi ed unc ion. To acili a e in e acing o he cus om ha dwa e he Colib i E alua ion Boa d p o ides he bu e ed CPU bus on a sepa a e connec o . De elopmen o he Fea u e Ex ac o o Speech Recogni ion 9 Figu e 2.2 – Colib i E alua ion Boa d Module Speci ica ions: CPU Modules Colib i PXA270 Colib i PXA300 Colib i PXA310 Colib i PXA320 In e aces 10/100MBi E he ne USB Hos /De ice USB Hos 2x PS/2 Analogue VGA Gene ic LCD Connec o TFT: Philips LB064V02-A1 Line-In, Line-Ou , Mic-In I DA 2x RS232 CAN (Philips SJA1000) SD Ca d Compac Flash Powe Supply: Requi ed Inpu : 7-24VDC, 3-50W On-boa d Con e e : 3.3V, 5V max 5A Size: 200 x 200 mm 10 Eneko Año ga, Diploma Wo k The ecei ed in oice om To adex, o he Colib i XScale® PXA320, plus he Colib i E alua ion Ca ie Boa d and plus he suppo hou s a e shown in he nex Figu e 2.3: Figu e 2.3 – In oice om To adex 2.1.2 So wa e The module is shipped wi h a p eins alled WinCE 5.0 image wi h WinCE Co e license. O he OS like Embedded Linux a e a ailable om he hi d-pa y. To adex p o ides a WinCE 5.0 image and a WinCE 6.0. All WinCE images con ain he To adex Boa d Suppo Package (BSP) which is one o he mos ad anced BSPs a ailable on he ma ke . Besides he s anda d Windows CE unc ionali y, i includes a la ge numbe o addi ional d i e s as well as op imized e sions o s anda d d i e s o he mos common in e aces and is easily cus omizable by egis y se ings o adap o speci ic ha dwa e. The Mic oso ® eMbedded Visual C++ 4.0 ool is used as desk op de elopmen en i onmen o c ea ing he applica ions and sys em componen s o Windows® CE .NET powe ed de ices. In conclusion, all he so wa e p esen ed in his wo k was done using he Mic oso ® eMbedded Visual C++ 4.0 de elopmen ool and he To adex BSP ool (Figu e 2.4). De elopmen o he Fea u e Ex ac o o Speech Recogni ion 11 Figu e 2.4 – Mic oso ® eMbedded Visual C++ 4.0 and Windows® CE 2.2 DESIGN OF THE FEATURE EXTRACTOR As s a ed be o e, in a speech ecogni ion p oblem he FE block has o p ocess he incoming in o ma ion, he speech signal, so ha i s ou pu eases he wo k o he classi ica ion s age. The app oach used in his wo k designs he FE block and di ides i in o wo consecu i e sub-blocks: he i s is based on speech coding echniques, and he second uses a SOM o u he op imiza ion (da a dimensionali y educ ion). The di e en blocks and sub-blocks a e shown in he nex Figu e 2.5: 12 Eneko Año ga, Diploma Wo k Figu e 2.5 – FE schema ic 2.2.1 Speech coding 2.2.1.1 Speech sampling The speech was eco ded and sampled using a ela i ely inexpensi e dynamic mic ophone and a Colib i’s audio inpu in e ace. The incoming signal was sampled a 8.000 Hz wi h 16 bi s o esolu ion. I migh be a gued ha a highe sampling equency, o mo e sampling p ecision, is needed in o de o highe ecogni ion accu acy. Howe e , i a no mal digi al phone, which samples speech a 8.000 Hz wi h a 16 bi esolu ion, is able o p ese e mos o he in o ma ion ca ied by he signal [6], i does no seem necessa y o inc ease he sampling a e beyond 8.000 Hz o he sampling p ecision o some hing highe han 16 bi s. Ano he eason behind hese se ings is ha De elopmen o he Fea u e Ex ac o o Speech Recogni ion 13 comme cial speech ecognize s ypically use compa able pa ame e alues and achie e imp essi e esul s. 2.2.1.2 P e-emphasis il e A e sampling he inpu signal is con enien o il e i wi h a second o de high-pass il e wi h cu o equency a 140 Hz. The il e se es as a p ecau ion agains undesi ed low- equency componen s. The esul ing il e is gi en by: ( ) 21 21 1 9114024 . 0 9059465 . 1 1 46363718.092724705.046363718.0 −− −− + − +− = z z zz zH h (1) 2.2.1.3 Wo d Isola ion Despi e he ac ha he sampled signal had pauses be ween he u e ances, i was s ill needed o de e mine he s a ing and ending poin s o he wo d u e ances in o de o know exac ly he signal ha cha ac e ized each wo d. To accomplish his, we decided o use VAD (Voice Ac i i y De ec ion) echnique used in speech p ocessing, ins ead o using he olling a e age and he h eshold, de e mined by he s a and end o each wo d, used in he p e ious wo ks, wi h he aim o achie ing mo e accu acy and e iciency. Fo ha , we based ou wo k in he Annex B om he ITU’s Recommenda ion G.729 [5], whe e a sou ce code in C language abou he VAD is e icien ly de eloped. VAD is a me hod which di e en ia es speech om silence o noise signal o aid in speech p ocessing and he Annex B p o ides a high le el desc ip ion o he Voice Ac i i y De ec ion (VAD), Discon inuous T ansmission (DTX) and Com o Noise Gene a o (CNG) algo i hms. These algo i hms a e used o educe he ansmission a e du ing silence pe iods o speech. They 14 Eneko Año ga, Diploma Wo k a e designed and op imized o wo k in conjunc ion wi h [ITU-T V.70]. [ITU-T V.70] manda es he use o speech coding me hods. The algo i hms a e adap ed o ope a e wi h bo h he ull e sion o G.729 and Annex B. Le ’s see a gene al desc ip ion o he VAD algo i hm: The VAD algo i hm makes a oice ac i i y decision e e y 10 ms in acco dance wi h he ame size o he p e-p ocessed ( il e ed) signal. A se o di e ence pa ame e s is ex ac ed and used o an ini ial decision. The pa ame e s a e he ull-band ene gy, he low-band ene gy, he ze o-c ossing a e and a spec al measu e. The long- e m a e ages o he pa ame e s du ing non- ac i e oice segmen s ollow he changing na u e o he backg ound noise. A se o di e en ial pa ame e s is ob ained a each ame. These a e a di e ence measu e be ween each pa ame e and i s espec i e long- e m a e age. The ini ial oice ac i i y decision is ob ained using a piecewise linea decision bounda y be ween each pai o di e en ial pa ame e s. A inal oice ac i i y decision is ob ained by smoo hing he ini ial decision. The ou pu o he VAD module is ei he 1 o 0, indica ing he p esence o absence o oice ac i i y espec i ely. I he VAD ou pu is 1, he G.729 speech codec is in oked o code/decode he ac i e oice ames. Howe e , i he VAD ou pu is 0, he DTX/CNG algo i hms desc ibed he ein a e used o code/decode he non-ac i e oice ames. 2.2.1.4 Speech coding A e he signal was sampled, he spec um was la ened, and he u e ances we e isola ed we ied o codi y i using he Linea P edic ion Coding (LPC) me hod [3]. In a a ie y o applica ions, i is desi able o comp ess a speech signal o e icien ansmission o s o age. Fo example, o accommoda e many speech signals in a gi en bandwid h o a cellula phone sys em, each digi ized speech signal is comp essed be o e ansmission. Fo medium o low bi - a e speech code s, LPC me hod is mos widely used. Redundancy in a speech signal is emo ed by passing he signal h ough a speech analysis il e . De elopmen o he Fea u e Ex ac o o Speech Recogni ion 15 The ou pu o he il e , e med he esidual e o signal, has less edundancy han he o iginal speech signal and can be quan ized by a smalle numbe o bi s han he o iginal speech. The sho - e m analysis and syn hesis il e s a e based on 10 h o de linea p edic ion (LP) il e s. The LP syn hesis il e is de ined as: ∑ = − + = 10 1 ˆ 1 1 )( ˆ1 i i i za zA (2) whe e â i , i = 1,...,10, a e he quan ized Linea P edic ion (LP) coe icien s. Sho - e m p edic ion o linea p edic ion analysis is pe o med once pe speech ame using he au oco ela ion me hod wi h a 30 ms (240 samples) asymme ic window. E e y 10 ms (80 samples), he au oco ela ion coe icien s o windowed speech a e compu ed and con e ed o he LP coe icien s using he Le inson-Du bin algo i hm. Then he LP coe icien s a e ans o med o he LSP domain o quan iza ion and in e pola ion pu poses. The in e pola ed quan ized and unquan ized il e s a e con e ed back o he LP il e coe icien s ( o cons uc he syn hesis and weigh ing il e s o each sub ame). The LP analysis window consis s o wo pa s: he i s pa is hal a Hamming window and he second pa is a qua e o a cosine unc ion cycle. The window is gi en by: ( ) ( ) 239...,002 159 2002 cos 0,...,199 399 2 cos 46.054.0        =      − =       − = n n n n nw lp π π (3) The e is a 5 ms look-ahead in he LP analysis which means ha 40 samples a e needed om he u u e speech ame. This ansla es in o an ex a algo i hmic delay o 5 ms a he encode s age. The LP analysis window applies o 120 samples om pas speech ames, 80 samples om 16 Eneko Año ga, Diploma Wo k he p esen speech ame, and 40 samples om he u u e ame. The windowing p ocedu e is illus a ed in Figu e 2.4. Figu e 2.6 – Windowing p ocedu e in LP analysis The di e en shading pa e ns iden i y co esponding exci a ion and LP analysis windows. The windowed speech: ( ) ( ) ( ) 0,...,239 = = ′ nnsnwns lp (4) is used o compu e he au oco ela ion coe icien s: ( ) ( ) ( ) 0,...,10 239 =−= ∑ = kkn'sn'sk kn (5) To a oid a i hme ic p oblems o low-le el inpu signals he alue o (0) has a lowe bounda y o (0) = 1.0. A 60 Hz bandwid h expansion is applied by mul iplying he au oco ela ion coe icien s wi h: ( ) 1,...,10 2 2 1 2 0 =                π −= k k expkw s lag (6) De elopmen o he Fea u e Ex ac o o Speech Recogni ion 23 1. The incoming p essu e wa e is educed o a digi al signal h ough a sampling p ocess. 2. The s a ing and he ending poin s o he u e ance embedded in o he signal a e ob ained using he VAD p ocess. 3. The spec um o he ex ac ed u e ance is enhanced by means o a p e-emphasis il e which boos s he high equency componen s. 4. Se e al da a blocks a e ex ac ed om he enhanced signal. 5. The ex ac ed da a blocks a e windowed o educe leakage e ec s. 6. LPC componen s a e ex ac ed om he il e ed blocks. 7. LPC Ceps um componen s a e hen ex ac ed om he LPC ec o s. 8. The dimensionali y o he LPC Ceps um ec o s is educed using a SOM. 9. The esul ing ec o s a e scaled i he Recognize equi es i . No hing can s ill be said abou he o e all e ec i eness o he FE block, since i depends on he ecogni ion accu acy o he o e all sys em. As an example, i he comple e sys em achie es low ecogni ion pe cen ages, ha can be caused by he FE block o he Recognize , bu , i i achie es highe pe cen ages ha means ha he FE block was a leas able o p oduce da a ha allowed hese ecogni ion accu acies. In o he wo ds, in o de o know he use ulness o he FE block, he Recognize ou pu s mus be ob ained i s . 24 Eneko Año ga, Diploma Wo k 2.3 SOFTWARE DEVELOPMENT 2.3.1 Audio Reco de and Playe As we ha e men ioned be o e, we buil a comple e audio Reco de and Playe . This ask was no s ic ly necessa y bu he aim o his has been o lea n, p ac ice and imp o e he C ++ p og amming skills. The g aphic in e ace o he p og am is shown in he ollowing Figu e 2.8: Figu e 2.8 – audioce Reco de & Playe De elopmen o he Fea u e Ex ac o o Speech Recogni ion 25 As we can see in he Figu e 2.8, wi h his so wa e we a e able o eco d he oice du ing some ime, wi h di e en sample a es, di e en esolu ions and inally we can sa e i in a .wa ile o nex p ocessing s eps like he speech coding. In addi ion, we will be able o play and lis en o he eco ded signals. Along wi h he documen a ion (Annex 2) is included an elec onic a achmen con aining he sou ce code in C++ used o build he so wa e and which is coming wi h all he necessa y explana ions. As we can see in he nex Figu e 2.9 we can see he di e ence be ween he di e en ways o sampling. In gene al, he memo y occupied by he sound ile is p opo ional o he numbe o samples pe second and he esolu ion o each sample. Fo he i s case he speech is sampled a 8.0 KHz and wi h 8 bi s o esolu ion (1 by e/sample), his means ha he memo y occupied o he sound ile will be 8.000 (samples/sec) x 5 (sec) = 40.000 (samples) = 40.000 (samples) x 1 (by e/sample) = 40 Kby es. Fo he second case he speech is sampled a 44.1 KHz and wi h 16 bi s o esolu ion (2 by es/sample), he memo y occupied o he sound ile will be 44.100 (samples/sec) x 5 (sec) = 220.500 (samples) = 220.500 (samples) x 2 (by es/sample) = 440.1 Kby es. 26 Eneko Año ga, Diploma Wo k Figu e 2.9 – Di e en samplings o he same speech signal I migh be a gued ha he highe he sampling equency and he highe he sampling p ecision, he be e he ecogni ion accu acy. Howe e , i a no mal digi al phone, which samples speech a 8.000 Hz wi h a 16 bi p ecision, is able o p ese e mos o he in o ma ion ca ied by he signal [6], i does no seem necessa y o inc ease he sampling a e beyond 8.000 Hz o he sampling p ecision o some hing highe han 16 bi s. Ano he eason behind hese se ings is ha comme cial speech ecognize s ypically use compa able pa ame e alues and achie e imp essi e esul s. So o he speech code we decided o use 8.000 Hz o sample a e and 16 bi s o esolu ion as con igu a ion o eco d he oice. De elopmen o he Fea u e Ex ac o o Speech Recogni ion 27 2.3.2 Fea u e Ex ac o A e buil he audioce Reco de and Playe we added some new unc ions o he p og am as he pa which ep esen s he FE block. Fo ha , we based ou wo k on he ITU-T’s G.729 Recommenda ion. This Recommenda ion con ains he desc ip ion o an algo i hm o he coding o speech signals using Linea P edic ion Coding and in which mo e p ocesses like he il e ing o he sampled signal, he di ision in o blocks and he windowing o il e ed signal and he wo d isola ion a e implici . The g aphic in e ace o he p og am is shown in he ollowing Figu e 2.10. Figu e 2.10 – audioce Reco de & Playe & VAD De ec o & Speech Code 28 Eneko Año ga, Diploma Wo k Wi h his so wa e we a e able o eco d and play oice signals and sa e hem in .wa iles. In addi ion, we will be able o de ec and isola e he oice command om he sound ile, c ea e a new ile wi h i and inally codi y o educe i s dimensionali y. Along wi h he documen a ion (Annex 2) is included an elec onic a achmen con aining he sou ce code in C++ used o build he so wa e and which is coming wi h all he necessa y explana ions. When we p ess he bu on “VAD De ec o ...” we ha e o selec a sound ile which con ains he eco ded oice. Then he sampled signal will go h ough all hese s eps: (Sampling: he oice signal is sampled a 8.000 He z wi h 16 bi s o p ecision and sa ed in a new ile called “le .wa ”.) 1. P e-emphasis il e : he sampled signal is il e ed by a second o de high-pass il e and sa ed in a new ile called “ il e ed.wa ”. 2. Wo d Isola ion: he il e ed signal is passed h ough he VAD block o isola e he wo d and sa ed in a ile called “ ad.wa ”. We can ep esen he esul s o he di e en s ages o he signal using Ma lab and he ea lie c ea ed sound iles (Figu e 2.11). De elopmen o he Fea u e Ex ac o o Speech Recogni ion 29 Figu e 2.11 – Di e en s ages o he signal in VAD De ec o p ocess As we can see, he i s g aphic shows he o iginal signal (Command “ igh ”), sampled a 8.0 KHz, wi h 16 bi s o esolu ion and in mono o 1 channel. We can ealize ha he signal has a DC o se and also ha he end o he eco ding, is a bi noisy. The second g aphic shows he il e ed sampled signal and inally he hi d g aphic shows he isola ed wo d, a e he VAD p ocess. A his poin we ha e o explain ha he esul shown o he VAD p ocess was ob ained using an algo i hm myVAD.m [10] ob aining p e y good esul s. The p oblem wi h he algo i hm de eloped o Colib i module is ha i does no a oid he silence and he non-speech pa s om he oice e y well as he myVAD.m algo i hm does, as is shown in he nex Figu e 2.12. 30 Eneko Año ga, Diploma Wo k Figu e 2.12 – Di e en ways o VAD p ocess In he abo e Figu e 2.12, he second g aphic shows he eco ded signal a e VAD p ocess using he algo i hm de eloped o Colib i module. He e, we can see ha e en hough he sound ile is educed in some silen pa s o he signal a e a oided; hese p ocess is no doing he co ec wo k as he hi d g aphic does. We ied o ind he solu ion o his bu inally we did no , so his pa need o be imp o ed and is le o u u e esea che s as we explain in he sec ion 3.2 Di ec ions o u u e esea ch. To con inue ou esea ch we decided o use he ile c ea ed ough Ma lab and myVAD.m algo i hm called “ ad2.wa ”. Once we ge he isola ed wo d (“ ad2.wa ”) we can s a codi ying he speech clicking in “Speech Code ...” bu on. The nex s eps a e he ones which he isola ed wo d signal will ollow: De elopmen o he Fea u e Ex ac o o Speech Recogni ion 31 1. Blocking: he isola ed wo d is di ided in o a sequence o da a blocks o ixed leng h, called ames and mul iplied by Hamming window o same wid h. 2. LPC analysis: o each ame, 10 LPC coe icien s a e calcula ed. 3. Ceps um analysis: he 10 LPC coe icien s a e con e ed in 10 Ceps al coe icien ones (Figu e 2.13). Figu e 2.13 – The 10 Ceps al coe icien s o each ame 32 Eneko Año ga, Diploma Wo k Once we ge he 10 Ceps al coe icien s o each ame i we click in “SOM...” bu on and selec he ile wi h he Ceps al coe icien s (“c_coe . x ”), we will ed he inpu laye o he Kohonen SOM, wi h his inpu da a one by one. The ou pu laye will o ganize i sel o ep esen he inpu s in wo-dimensional space. The aining p ocedu e in ol es he ollowing s eps: 1. The neu ons a e a anged in an n-dimensional la ice. Each neu on s o es a poin in an m- dimensional space. 2. An inpu ec o is p esen ed o he SOM. The neu ons s a o compe e un il he one ha s o es he closes poin o he inpu ec o p e ails. Once he dynamics o he ne wo k con e ge, all he neu ons bu he p e ailing one will be inac i e. The ou pu o he SOM is de ined as he co-o dina es o he p e ailing neu on in he la ice. 3. A neighbou hood unc ion is cen ed on he p e ailing neu on o he la ice. The alue o his unc ion is one a he posi ion o he ac i e neu on, and dec eases wi h he dis ance measu ed om he posi ion o he winning neu on. 4. The poin s s o ed by all he neu ons a e mo ed owa ds he inpu ec o in an amoun p opo ional o he neighbou hood unc ion e alua ed in he posi ion o he la ice whe e he neu on being modi ied s ands. 5. Re u n o 2, and epea s eps 2, 3, and 4 un il he a e age e o be ween he inpu ec o s and he winning neu ons educes o a small alue. A e he SOM is ained, he co-o dina es o he ac i e neu on in he la ice a e used as i s ou pu s. Along wi h he documen a ion (Annex 2) is included an elec onic a achmen con aining he sou ce code in C++ used o build he so wa e and which is coming wi h all he necessa y explana ions. In his case pa o he code o SOM has been de eloped bu s ill needs o be imp o ed and inished, so his pa is le o u u e esea che s as we explain in he sec ion 3.2 Di ec ions o u u e esea ch. De elopmen o he Fea u e Ex ac o o Speech Recogni ion 39 ANNEX 2: 40 Eneko Año ga, Diploma Wo k DECLARATION: I, Eneko Año ga, he unde signed, decla e ha I ha e made he diploma wo k by mysel . I am awa e o he po en ial consequences in he e en o a b each o his decla a ion.