Voice Recognition Using Artificial Neural Network

International Journal of Advance Foundation and Research in Computer (IJAFRC)
30 | © 20!" IJAFRC All Ri#hts Reserved
$oice Reco#nition %sin# Artificial &eural &et'or(
)upri*a +atil

",erric( +ereira
1,2,3,4,5
Electronics and Telecommunication , Padre Conceicao College of Engineering,Verna, Goa, India
E-mail: suri!a"adi#rediff$com
rac%isatarde&ar3#gmail$com
A - ) . R A C .

$oice is the sound produced in a person/s lar*n0 and uttered throu#h the mouth" as speech or
son#1 2i(e fin#er prints and the iris of the e*e the voice of each person is uni3ue1 .he parameters
of a person4s voice differ in pitch" tim5er and vocal intensit*1 6ence it can 5e
authentication1 In this paper feature e0traction methods used are 2inear +redictive Codin#" Fast
Fourier .ransform and 'avelet transform to e0tract the a5ove mentioned features from a
data5ase of voices that 'ere recorded1 Artificial neural net'or
of classification1 )ome of the applications are Forensic applications" Artificial intelli#ence" Access
Control" .ransaction Authentication" 2a' 7nforcement and )peech ,ata 8ana#ement1

Inde0 .eams9 A&&9 Artificial &eural
:avelet .ransform" FF.9 Fast Fourier .ransform
I1 I&.R;,%C.I;&
T%e 'oice is roduced in a erson(s lar!n) and uttered t%roug% t%e mout%, as seec% or song$ T%e t%ree
different arameters t%at ma&e a
'oice*,itc%+fre,uenc! reresentation -%ic% deends on seed of 'i.ration of our 'ocal c%ord* and
tim.er + ,ualit! in a ersons 'oice and it is measured from t%e en'eloe of t%e o-er sectru
-%ic% is different for eac% erson*$




II1 +R;C7,%R7 ;$7R$I7:
In t%is aer met%ods used for feature e)tracti
and /iscrete -a'elet Transform$ 0rtificial neural
A1 Feature 70traction
1 Fast Fourier .ransform (FF.)
0 1ast 1ourier Transform is an algorit%m to comute discrete 1ourier Transform and its in'ers
1ourier Transform con'erts time domain to fre,uenc! domain and 'ice
suc% Transform$ 0s a result 11T are -id
mat%ematics$ 11T gi'es t%e e)act same result as /1T does, t%e onl! difference is t%at 11T does it
T%e se,uence of 2 comle) num.ers
according to t%e /1T formula:
ournal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1

© 20!" IJAFRC All Ri#hts Reserved
$oice Reco#nition %sin# Artificial &eural &et'or(
",erric( +ereira
2
"=arr* ,a Costa
3
"+rachi )atarde(ar
!
">anshila Rodri#ues
Electronics and Telecommunication , Padre Conceicao College of Engineering,Verna, Goa, India
suri!a"adi#rediff$com, derric&ereira14#gmail$com, garr!dacosta13#gmail$com
rac%isatarde&ar3#gmail$com, 3ans%ilarod24#gmail$com
produced in a person/s lar*n0 and uttered throu#h the mouth" as speech or
son#1 2i(e fin#er prints and the iris of the e*e the voice of each person is uni3ue1 .he parameters
of a person4s voice differ in pitch" tim5er and vocal intensit*1 6ence it can 5e
authentication1 In this paper feature e0traction methods used are 2inear +redictive Codin#" Fast
Fourier .ransform and 'avelet transform to e0tract the a5ove mentioned features from a
data5ase of voices that 'ere recorded1 Artificial neural net'or( classifier operates on the result
of classification1 )ome of the applications are Forensic applications" Artificial intelli#ence" Access
Control" .ransaction Authentication" 2a' 7nforcement and )peech ,ata 8ana#ement1
A&&9 Artificial &eural &et'or(" 2+C9 2inear +redictive Codin#
Fast Fourier .ransform
T%e 'oice is roduced in a erson(s lar!n) and uttered t%roug% t%e mout%, as seec% or song$ T%e t%ree
different arameters t%at ma&e a 'oice different are loudness+total energ! contained in a ersons
'oice*,itc%+fre,uenc! reresentation -%ic% deends on seed of 'i.ration of our 'ocal c%ord* and
tim.er + ,ualit! in a ersons 'oice and it is measured from t%e en'eloe of t%e o-er sectru

Fi#ure 1 -loc( ,ia#ram
used for feature e)traction are 1ast 1ourier Transform, 5inear Predicti'e C
0rtificial neural net-or& is used for classification$

1ourier Transform is an algorit%m to comute discrete 1ourier Transform and its in'ers
1ourier Transform con'erts time domain to fre,uenc! domain and 'ice 'ersa$ 0n 11T raidl! comutes
suc% Transform$ 0s a result 11T are -idel! used for man! alications in engineering, science and
mat%ematics$ 11T gi'es t%e e)act same result as /1T does, t%e onl! difference is t%at 11T does it
comle) num.ers is transformed into 2 comle) num.ers
according to t%e /1T formula:
ournal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1 I))& 23!< ? !<@3
'''1iAafrc1or#
$oice Reco#nition %sin# Artificial &eural &et'or(
">anshila Rodri#ues
@

Electronics and Telecommunication , Padre Conceicao College of Engineering,Verna, Goa, India
garr!dacosta13#gmail$com
3ans%ilarod24#gmail$com

produced in a person/s lar*n0 and uttered throu#h the mouth" as speech or
son#1 2i(e fin#er prints and the iris of the e*e the voice of each person is uni3ue1 .he parameters
of a person4s voice differ in pitch" tim5er and vocal intensit*1 6ence it can 5e used for
authentication1 In this paper feature e0traction methods used are 2inear +redictive Codin#" Fast
Fourier .ransform and 'avelet transform to e0tract the a5ove mentioned features from a
( classifier operates on the result
of classification1 )ome of the applications are Forensic applications" Artificial intelli#ence" Access
Control" .ransaction Authentication" 2a' 7nforcement and )peech ,ata 8ana#ement1
2+C9 2inear +redictive Codin#" ,:.9 ,iscrete

T%e 'oice is roduced in a erson(s lar!n) and uttered t%roug% t%e mout%, as seec% or song$ T%e t%ree
'oice different are loudness+total energ! contained in a ersons
'oice*,itc%+fre,uenc! reresentation -%ic% deends on seed of 'i.ration of our 'ocal c%ord* and
tim.er + ,ualit! in a ersons 'oice and it is measured from t%e en'eloe of t%e o-er sectrum o.tained
ast 1ourier Transform, 5inear Predicti'e Coding
net-or& is used for classification$
1ourier Transform is an algorit%m to comute discrete 1ourier Transform and its in'erse$ 0
'ersa$ 0n 11T raidl! comutes
in engineering, science and
mat%ematics$ 11T gi'es t%e e)act same result as /1T does, t%e onl! difference is t%at 11T does it faster$
ransformed into 2 comle) num.ers
International Journal of Advance Foundation and Research in Computer (IJAFRC)
3 | © 20!" IJAFRC All Ri#hts Reserved
E'aluating t%is definition directl! re,uires 6+22* oerations: t%ere are
re,uires a sum of 2 terms$ 0n 11T is an! met%od to comute t%e same results in 6+2
7! far t%e most commonl! used 11T is t%e
algorit%m t%at recursi'el! .rea&s do-n a /1T of an!
si3es 21 and 22, along -it% 6+2* multilications .! comle)
n* returns t%e n-oint /1T$ 11T+8*
singleton dimension$ If t%e lengt% of
lengt% of 8 is greater t%an n, t%e se,uence
are ad9usted in t%e same manner$
21 2inear +redictive Codin# (2+C)
5inear Predicti'e Coding +5PC* is a tool used mostl! in
reresenting t%e sectral en'eloe
information of a linear redicti'e model$ It is one of t%e most o-erful 'oice anal!sis tec%ni,ues, and one
of t%e most useful met%ods for encoding good ,ualit! 'oice at a lo- .it rate and ro'ides e)tremel!
accurate estimates of 'oice arameters$
remo'ing t%eir effects from t%e 'oice signal, and estimating t%e intensit! and fre,uenc! of t%e remaining
.u33$
Fi#ure 21
0s er t%e 1ig2 ast 'oice samle is used to redict t%e
current 'oice samle s+n* and t%e linearl! redicted 'oice samle
error signal e+n* !ou find t%e :1ilter Coefficients;$
T%e general formula for t%e signal <+n* is
T%erefore <+n* can .e redicted from a linearl! -eig%ted summation of t%e ast 'oice samles$ 2o- t%e
error signal e+n* gi'es onl! t%e amlitude information$ To e)tract t%e itc% +or fre,uenc!* information of
t%e 'oice signal t%e 1ast 1ourier Transform +11T* of t
filter coefficients are not dominant in t%e time domain, it is onl! -%en t%e 11T of t%e error signal is ta&en
t%e coefficients are seen in t%eir roer domain +fre,uenc! domain*$ T%ese filter coefficients
fre,uenc! domain are gi'en to t%e artificial neural net-or& for furt%er rocessing$
31 ,iscrete :avelet .ransform (,:.)
In discrete -a'elet Transform at eac% decomosition le'el, t%e %alf .and filters roduce signals sanning
onl! %alf t%e fre,uenc! .and$ In accordance -it% 2!,uist;s rule if t%e original signal %as a %ig%est
ournal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1

© 20!" IJAFRC All Ri#hts Reserved

directl! re,uires 6+22* oerations: t%ere are 2 oututs
terms$ 0n 11T is an! met%od to comute t%e same results in 6+2
7! far t%e most commonl! used 11T is t%e Coole!=Tu&e! algorit%m$ T%is is a
.rea&s do-n a /1T of an! comosite si3e 2 > 2122 into man! smaller /1Ts of
6+2* multilications .! comle) roots of unit!$ In
11T+8* is e,ui'alent to 11T+8, n* -%ere n is t%e si3e of
gleton dimension$ If t%e lengt% of 8 is less t%an n, 8 is added -it% trailing 3eros to lengt%
n, t%e se,uence 8 is truncated$ ?%en 8 is a matri), t%e lengt% of t%e columns
redictive Codin# (2+C)
oding +5PC* is a tool used mostl! in audio signal rocessing and 'oice recognition for
sectral en'eloe of a digital signal of a 'oice samle in comressed
model$ It is one of t%e most o-erful 'oice anal!sis tec%ni,ues, and one
of t%e most useful met%ods for encoding good ,ualit! 'oice at a lo- .it rate and ro'ides e)tremel!
accurate estimates of 'oice arameters$ 5PC anal!3es t%e 'oice signal .! estimating t%e formants,
remo'ing t%eir effects from t%e 'oice signal, and estimating t%e intensit! and fre,uenc! of t%e remaining

ure 21 2inear +redictor -loc( ,ia#ram
0s er t%e 1ig2 ast 'oice samle is used to redict t%e ne- 'oice samle <+n*$ T%e difference .et-een
current 'oice samle s+n* and t%e linearl! redicted 'oice samle <+n* gi'es t%e error e+n*$ @sing t%e
error signal e+n* !ou find t%e :1ilter Coefficients;$
<+n* is

<+n* can .e redicted from a linearl! -eig%ted summation of t%e ast 'oice samles$ 2o- t%e
error signal e+n* gi'es onl! t%e amlitude information$ To e)tract t%e itc% +or fre,uenc!* information of
1ourier Transform +11T* of t%e error signal is ta&en$ T%e reason .eing t%e 5PC
filter coefficients are not dominant in t%e time domain, it is onl! -%en t%e 11T of t%e error signal is ta&en
t%e coefficients are seen in t%eir roer domain +fre,uenc! domain*$ T%ese filter coefficients
fre,uenc! domain are gi'en to t%e artificial neural net-or& for furt%er rocessing$
(,:.)
In discrete -a'elet Transform at eac% decomosition le'el, t%e %alf .and filters roduce signals sanning
onl! %alf t%e fre,uenc! .and$ In accordance -it% 2!,uist;s rule if t%e original signal %as a %ig%est
ournal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1 I))& 23!< ? !<@3
'''1iAafrc1or#
oututs 8&, and eac% outut
terms$ 0n 11T is an! met%od to comute t%e same results in 6+2 log 2* oerations$
algorit%m$ T%is is a di'ide and con,uer
into man! smaller /1Ts of
In A0T507 B > 11T+8 ,
is t%e si3e of 8 in t%e first non-
is added -it% trailing 3eros to lengt% n$ If t%e
is a matri), t%e lengt% of t%e columns
and 'oice recognition for
comressed form, using t%e
model$ It is one of t%e most o-erful 'oice anal!sis tec%ni,ues, and one
of t%e most useful met%ods for encoding good ,ualit! 'oice at a lo- .it rate and ro'ides e)tremel!
'oice signal .! estimating t%e formants,
remo'ing t%eir effects from t%e 'oice signal, and estimating t%e intensit! and fre,uenc! of t%e remaining
<+n*$ T%e difference .et-een
<+n* gi'es t%e error e+n*$ @sing t%e
<+n* can .e redicted from a linearl! -eig%ted summation of t%e ast 'oice samles$ 2o- t%e
error signal e+n* gi'es onl! t%e amlitude information$ To e)tract t%e itc% +or fre,uenc!* information of
%e error signal is ta&en$ T%e reason .eing t%e 5PC
filter coefficients are not dominant in t%e time domain, it is onl! -%en t%e 11T of t%e error signal is ta&en
t%e coefficients are seen in t%eir roer domain +fre,uenc! domain*$ T%ese filter coefficients in t%e
fre,uenc! domain are gi'en to t%e artificial neural net-or& for furt%er rocessing$
In discrete -a'elet Transform at eac% decomosition le'el, t%e %alf .and filters roduce signals sanning
onl! %alf t%e fre,uenc! .and$ In accordance -it% 2!,uist;s rule if t%e original signal %as a %ig%est
International Journal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1 I))& 23!< ? !<@3

32 | © 20!" IJAFRC All Ri#hts Reserved '''1iAafrc1or#
fre,uenc! of -, -%ic% re,uires a samling fre,uenc! of 2- radians, t%en it no- %as a %ig%est fre,uenc!
of -C2 radians$ It can no- .e samled at a fre,uenc! of - radians t%us discarding %alf t%e samles -it%
no loss of information$ T%is decimation .! 2 %al'es t%e time resolution as t%e entire signal is no-
reresented .! onl! %alf t%e num.er of samles$ T%us, -%ile t%e %alf .and lo- ass filtering remo'es %alf
of t%e fre,uencies and t%us %al'es t%e resolution, t%e decimation .! 2 dou.les t%e scale$ ?it% t%is
aroac%, t%e time resolution .ecomes ar.itraril! good at %ig% fre,uencies, -%ile t%e fre,uenc!
resolution .ecomes ar.itraril! good at lo- fre,uencies$

Fi#ure 31 +rocess of ,:.
0ccording to 1ig3 /?T is comuted .! successi'e lo- ass and %ig% ass filtering of t%e discrete time-
domain signal$ 0t eac% le'el, t%e %ig% ass filter roduces detailed information -%ile t%e lo- ass filter
associated -it% scaling function roduces coarse aro)imation$
III1 AR.IFICIA2 &7%RA2 &7.:;RB
A1 -ac( propa#ation al#orithm

Fi#ure !1 -ac( propa#ation process
0s er 1ig4 outut for t%e resented inuts is calculated$ T%e error -%ic% is found .! comaring t%e
actual outut -it% t%e desired outut$ T%e errors are assed .ac& t%roug% t%e neural net-or& .!
comuting t%e contri.ution of eac% %idden rocessing unit and deri'ing t%e corresonding ad9ustment
needed to roduce t%e correct outut$ T%e connection -eig%ts are t%en ad9usted and t%e neural net-or&
%as 9ust DlearnedD from an e)erience$
III1 I8+2787&.A.I;&

In t%is aer a data.ase of 'oices is made using PCA Eecorder, -%ic% is an android alication t%at
records 'oice samles at FGGGH3+Aono*$ In t%is aer fi'e 'oice samles of fi'e different eole are
recorded$ T%ese 'oice signals toget%er ma&e u t%e data.ase$ 0ll t%e 'oice signals are used .! A0T507 in
real-time from t%e sa'ed data.ase in t%e comuter$ 7! setting a t%res%old of a articular 'alue t%e noise
is remo'ed$ T%en a feature e)traction is erformed using linear redicti'e coding, 1ast 1ourier
Transform and /iscrete ?a'elet Transform, searatel!$ T%en t%e samles of t%e t%ree 'oice signals of
eac% erson are used to ma&e t%e training data s%eets and t%e samles of t%e remaining t-o 'oice signals
of eac% erson are used to ma&e t%e testing data s%eets$ 1ig5 s%o-s %o- t%e data.ase is di'ided$

International Journal of Advance Foundation and Research in Computer (IJAFRC)
33 | © 20!" IJAFRC All Ri#hts Reserved
Fi#ure @1 -loc( dia#ram indicatin# the ma(in# of the trainin# and testin# data sheets
I$1 R7)%2.)
T%e result of classification .ased on different feature e)traction met%ods are as follo-s:
Feature 70traction 8ethod
/?T d.4
/?T d.I

$1 A++2ICA.I;&)
1$ Forensic applications-Voice recognition using 022 can .e used in t%e cases of &idnaing
t%e criminal can .e identified
2$ Access Control applications
information to usual a ass-ord*$
3$ .ransaction Authentication applications
aut%entication is ac%ie'ed using 'oice recognition using 022$
4$ +ersonaliCation applications
t%e o-ner of a mo.ile or a car$
$I1 2I8I.A.I;&)
• ?%ile recording t%e data.ase if noise is a.o'e t%e t%res%old, t%en it -ill .e considered as art of
t%e erson;s 'oice signal and -ill cause ro.lems later in t%e s!stem$
• Peole can mimic t%e stored 'oices and in t%is -a! an intruder -ould .e declared as t%e correct
erson$

$II1 C;&C2%)I;&
5PC and 11T are most effecti'e feature e)traction met%ods for 'oice recognition$ 7! t%ese met%ods itc%
and 'ocal $intensit! of a erson;s 'oice are t%e .est suited arameters for identif!ing an indi'idual$
$III1 R7F7R7&C7)
J1K /igital Lignal Processing Pri
ournal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1

© 20!" IJAFRC All Ri#hts Reserved

-loc( dia#ram indicatin# the ma(in# of the trainin# and testin# data sheets
T%e result of classification .ased on different feature e)traction met%ods are as follo-s:
.a5le 9 Results
70traction 8ethod Accurac*
11T
5PC
/?T d.4
/?T d.I
FFM
I2M
FGM
FGM
Voice recognition using 022 can .e used in t%e cases of &idnaing
t%e criminal can .e identified from a &no-n ool of criminal data.ases$
Access Control applications- 1or control access to comuter net-or&s +adds .iometric
information to usual a ass-ord*$
.ransaction Authentication applications- In Tele%one .an&ing %ig%er le'els of secure
aut%entication is ac%ie'ed using 'oice recognition using 022$
applications- Ao.iles and Cars can used Voice recognition using 022 to identif!
t%e o-ner of a mo.ile or a car$
?%ile recording t%e data.ase if noise is a.o'e t%e t%res%old, t%en it -ill .e considered as art of
d -ill cause ro.lems later in t%e s!stem$
Peole can mimic t%e stored 'oices and in t%is -a! an intruder -ould .e declared as t%e correct
5PC and 11T are most effecti'e feature e)traction met%ods for 'oice recognition$ 7! t%ese met%ods itc%
and 'ocal $intensit! of a erson;s 'oice are t%e .est suited arameters for identif!ing an indi'idual$
/igital Lignal Processing Princiles, 0lgorit%ms, and 0llications .! No%n G$Proa&is
ournal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1 I))& 23!< ? !<@3
'''1iAafrc1or#
-loc( dia#ram indicatin# the ma(in# of the trainin# and testin# data sheets
T%e result of classification .ased on different feature e)traction met%ods are as follo-s:
Voice recognition using 022 can .e used in t%e cases of &idnaing$ ?%ere
control access to comuter net-or&s +adds .iometric
In Tele%one .an&ing %ig%er le'els of secure
Ao.iles and Cars can used Voice recognition using 022 to identif!
?%ile recording t%e data.ase if noise is a.o'e t%e t%res%old, t%en it -ill .e considered as art of
Peole can mimic t%e stored 'oices and in t%is -a! an intruder -ould .e declared as t%e correct
5PC and 11T are most effecti'e feature e)traction met%ods for 'oice recognition$ 7! t%ese met%ods itc%
and 'ocal $intensit! of a erson;s 'oice are t%e .est suited arameters for identif!ing an indi'idual$
nciles, 0lgorit%ms, and 0llications .! No%n G$Proa&is
International Journal of Advance Foundation and Research in Computer (IJAFRC)
$olume " Issue <" Au#ust20!1 I))& 23!< ? !<@3

3! | © 20!" IJAFRC All Ri#hts Reserved '''1iAafrc1or#
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