Apr2015

ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) : 2045 -869X

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING APRIL 2015

VOL -5 NO -4

@IJITCE Publication

@IJITCE Publication

@IJITCE Publication

@IJITCE Publication

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015

www.ijitce.co.uk

IJITCE PUBLICATION

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.5 No.4 April 2015

www.ijitce.co.uk

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015

From Editor's Desk Dear Researcher, Greetings!

Research article in this issue discusses about motivational factor analysis.

Let us review research around the world this month.

Neuroscientists find that Cognitive Skills Peak at different ages a new research from neuroscientists and Massachusetts General Hospital reveals that different parts of the brain work best at different ages. Scientists have long known that our ability to think quickly and recall information also known as fluid intelligence, peaks around age 20 and then begins a slow decline. However, more recent findings, including a new study from neuroscientists at MIT and Massachusetts General Hospital (MGH), suggest that the real picture is much more complex. The researchers also included a vocabulary test, which serves as a measure of what is known as crystallized intelligence the accumulation of facts and knowledge. Gathering more data from their websites and have added new cognitive tasks designed to evaluate social and emotional intelligence, language skills and executive function. They are also working on making their data public so that other researchers can access it and perform other types of studies and analyses.

Speech recognition (SR) is the inter-disciplinary sub-field of computational linguistics which incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields to develop methodologies and technologies that enables the recognition and translation of spoken language into text by computers and computerized devices such as those categorized as Smart Technologies and robotics. It is also known as automatic speech recognition (ASR), computer speech recognition or just speech to text (STT). Some SR systems use training where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called speaker independent systems.

It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

Thanks, Editorial Team IJITCE

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015

Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia.

Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 Dr.P.UmaMaheswari Prof &Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore.

Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India.

Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India.

Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra.

(India).

Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ.

Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,RuaItapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.

Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India.

Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India.

Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India.

Mr. Md. Musfique Anwar B.Sc(Engg.)

Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh.

Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom.

Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India.

Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India.

Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. SeraphinChallyAbou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 55812-3042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India.

MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol"Noshirvani" University of Technology, Iran.

Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center,Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India.

Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering,Punjab Technical University,GianiZail Singh Campus,Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education,Virovitica College,MatijeGupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education,The University of Mississippi,Department of Leadership and Counselor Education, 139 Guyton University, MS 38677

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015

Contents A Novel Technique for Speech Identification and Recognition using Row and Column Features [RCF] G.Balakrishnan & Dr.S.Pannirselvam ……………….……………………………….……………………….[268]

www.ijitce.co.uk

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015

A Novel Technique for Speech Identification and Recognition using Row and Column Features (RCF) G.Balakrishnan* Ph.D Research Scholar & Associate Professor, Department of Computer Science, Navarasam College of Arts & Science for Women, Erode Email: balakrish1972@gmail.com Dr.S.Pannirselvam Research Supervisor & Head Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India.

Email: pannirselvam08@gmail.com Abstract— Today, image processing penetrates into various fields, but till it is struggling in identification and recognition issues. Speech recognition is developed into a very active research area specializing on how to extract and recognize within images. The text based Speech identification and recognition is widely used biometric application for security and identification concern. The various methods have been proposed for speech identification and recognition each method has advantages and drawbacks. The complexity in identification and recognition, other issues affects performance of existing system makes insufficient. In this paper presents speech identification and recognition on full image and on Row suggest of an image. In each of the methods, effect of different quantity of coefficients of transformed picture is determined. The Row and Column Feature (RCF) vector are calculated separately and stored.

The feature is generated and matching is done by Euclidean distance classification is used to measure a distance between diagnosed speech. The experimental result shows that RCF provides better recognition rate when compared with the existing methods.

Keywords— DCT, WALSH, HAAR, RCF.

1. INTRODUCTION Security protection has become an exceedingly vital problem due to widespread use of Net technology as well as because of multi-user applications. Identifying customers and granting get admission to only to those users who are authorized is a key to provide security. Users can be recognized the use of numerous strategies and their combinations. Because the generation is getting advanced, extra state-of-the-art approaches are being used to satisfy the want of safety. Speech identity problem may be further labeled as textual content based and text independent speech identity based totally on relevance to speech contents. Text dependent speech identity calls for the speech pronouncing precisely the enrolled or the given password/speech.

Textual content impartial speech identity is a system of verifying the identity without constraint on the speech content material. Speech identification assignment also can be categorized into closed set and open set speech identity. In closed set hassle, from N acknowledged audio system, the Speech whose reference template has the maximum diploma of similarity with the template of input speech sample of unknown Speech is received. This unknown speech is

assumed to be one of the given set of speech. As a result in closed set problem, system makes a compelled selection by selecting the best matching speech from the speech database.

In the open set, text structured speech identity matching reference template for an unknown audio system speech pattern may not exist.

2. LITERATURE SURVEY Speech identity trouble essentially includes characteristic extraction level and pattern class stage. In literature there are many strategies to be had for Speech identity process based totally on various processes for feature extraction.

Davis [1] proposed one of the famous procedures for feature extraction is the Mel Frequency Cepstrum Coefficients (MFCC). The MFCC parameter as by means of describes the power distribution of speech sign in a frequency area.

Wang Yutai et.al. [2] proposed a Speech popularity device based on dynamic MFCC parameters. This approach combines the Speech data received by MFCC with the pitch to dynamically construct a fixed of the Mel-filters. Those Melfilters are in addition used to extract the dynamic MFCC parameters which constitute characteristics of speech identity.

Sleit et al. [3] proposed a histogram primarily based technique turned into by way uses a reduced set of functions generated using MFCC method. For those features, histograms are created the use of predefined c programming language length. Histograms are generated first for all records in function set for each Speech and then for each characteristic column in feature set of every Speech.

Every other extensively used technique for feature extraction is located of linear Prediction Coefficients (LPC).

LPCs capture the facts about brief time spectral envelope of speech. LPCs constitute critical speech traits inclusive of formant speech frequency and bandwidth [4].

Vector Quantization (VQ) is yet another technique of function extraction based totally Speech popularity structures every Speech is characterized with numerous prototypes called code vectors [5].

Pati et al. [6] developed Speech recognition based totally on non-parametric vector quantization. Speech is produced due to excitation of vocal tract. In this technique, excitation records may be captured using LP analysis of

268

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 speech signal and is known as LP residual. This LP residual is in addition subjected to nonparametric Vector Quantization to generate codebooks of sufficiently massive length.

3. EXISTING METHODOLOGY 3.1 Discrete Cosine Transform (DCT) DCT image is split into blocks. However it gets rid of correlation throughout the bounds and subsequently consequences in blockading artifacts. This disadvantage may be averted by means of the use of wavelet transforms. Its extremely good strength compaction assets have made wavelets extra famous in current years. Extra energy compaction gives higher compression ratio.

3.1 Walsh Transform It is non-sinusoidal orthogonal transform that decomposes a signal into a set of orthogonal square waveforms called Walsh capabilities. The transformation has no multipliers and is real due to the fact the amplitude of Walsh features has best two values +1 or - 1. Walsh functions are square or rectangular waveforms with values of -1 or +1.

An essential function of Walsh capabilities is sequenced that's decided from the wide variety of 0-crossings per unit time interval. Every Walsh function has a completely unique sequence price.

3.3 Haar Transform This sequence becomes proposed in 1909 by means of Alfred Haar. Haar used these features to provide an example of a countable orthonormal machine for the distance of square-integrable functions at the real line. The Haar remodel is derived from the Haar matrix.

3.3 PROPOSED METHODOLOGY The first step within the Speech identification system to transform the speech signal into a wave. A wave format is time-various spectral illustration that indicates how the spectral density of a sign varies with time.

Text speech is commonly created in one of two methods: approximated as a clear out financial institution that consequences from a chain or calculated from the time signal using the short-time Fourier rework. Increasing a wave the use of sampled records, within the time domain is damaged up into portion, which normally overlap and Fourier transformed to calculate the importance of the frequency spectrum for each portion. The speech sign is first divided into frames is arranged column smart to form a matrix. Divide the every frame samples with an overlap of 25% between consecutive frames. These frames are then arranged column quick to form a matrix. The feature is then plotted as the squared magnitude of this column matrix.

Each transform is carried out on full image and from the feature vectors obtained, one of a different numbers of coefficients have been used to pick out Speech. Second, transform is carried out to row mean of an image to get the function vector of an image. From this feature vector again identification rate is acquired for various portions selected from the feature vector. Speech has been used as trainee images and testing speech images.

In this approach, transformation method has been applied on complete image to attain feature vector of image.

Further it decided on partial feature vectors, identification rate

changed into received. This option of function vector is primarily based on the wide variety of rows and columns that we selected from the characteristic vector of image. For these exclusive sizes, identity price changed into acquired. The row mean of these image are calculated and then transformation strategies have been carried out to them to form feature vectors of images and also images have been divided into N equal and non-overlapping blocks. Row mean of those blocks became calculated to get characteristic vectors of images.

Test Speech file

Reference Speech file

Feature Extraction

Convert Column Matrix

Convert Image Format Convert Text

Euclidean Distance

Recognized Speech

Fig.1 Process Flow 3.3.2 FEATURE VECTOR EXTRACTION The feature vectors of all of the reference speech samples are stored in the database inside the segment. The matching segment, the check sample this is to be diagnosed is taken and similarly processed as within the training phase to form the characteristic vector. The saved characteristic vector which gives the minimal Euclidean distance with the input pattern function vector is said as the Speech identified. The process for feature vector extraction is Column transform is implemented. The speech sign after which suggest of the absolute values of the rows of the remodel matrix is then calculated. Those row approach form column vector paperwork the characteristic vector for the speech sample are calculated for extraordinary values of n and saved within the database.

3.3.2.1 WAVE File Format Waveform Audio File Format (WAVE) is an application of RIFF or Resource Interchange File Format which stores audio bit streams in “amy”. WAVE encodes the sound in Linear Pulse Code Modulation format. Sound is basically a pressure wave or mechanical energy having pressure variance in an elastic medium. The variance propagates as compression and rarefaction wherein compression occurs when pressure is higher than the ambient pressure and rarefaction occurs when the pressure of the propagating wave is less than the ambient pressure. Exactly in the same manner a WAVE file just represents the sampled sound waves. In this work using an “amy.wav” wave file to show the proposed algorithm of encrypting the sound file in various image formats. As already mentioned a wave file consists of positive and negative values over its entire range of samples. Here for simplicity will using only the samples having positive values.

269

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 3.3.2.2 Image Formats Digital image formats are means of storing digital images in either uncompressed, compressed and vector formats. On rasterization an image is converted into a grid of pixels. In lossless compression the entire digital data is preserved during compression thus preserving image quality.

In lossy compressions, the digital data preservation takes place by compromising image quality. Here discussed only for JPEG formats and these are the very formats in which the wave files.

Perform procedure training_ feature ( ) { Step 1: Apply the transformation on resized image to obtain its feature vector.

Step 2: Save these feature vectors for further comparison.

Step 3: Read the query image.

Step 4: Repeat step 1 to step 3 for each training image in the database to extract their feature vector.

Step 5: Perform procedure Eucli_Dist () { Compute the distance measures for number of images from IDB with the target image using the equation 5.1. } Step 6: Declare the Speech corresponding to this trainee image as identified Speech.

Step 7: Repeat the Step 5 and Step 6 are repeated for selected portion of feature vector.

Step 8: Return }

3.3.2.3 Data of wave file in column matrix The wave file with graphical representation is provided with the sampling length of this tone and as discussed above using only those samples which have positive values. MATLAB code which fetches the wave file using ‘wavread’ function. Amplitude values are obtained in the range of 0 and +1. It is to be noted that the variable D is basically a column vector.

3.3.2.4 Convert column matrix into M x N matrix A grayscale image of M by N pixels is represented in MATLAB as an M X N matrix having “double” data type wherein each element of the matrix denotes a pixel within an intensity of 0 and 1. It is to be noted that the variable D is a column matrix with “double” type and intensity within 0 and 1. So to convert variable D in an image format have to transform D into a 1000 X 2000 matrix.

Perform procedure testing_ feature ( ) { Step 1: Apply the transformation on row mean to obtain its feature vector.

Step 2: Save feature vectors for further comparison.

Step 3: Read the query image.

Step 4: Repeat step 1 to step 3 for each test image in the database to extract their feature vector.

Step 5: Perform procedure Eucli_Dist () { Compute the distance measures for number of images from IDB with the target image using the equation 5.1. } Step 6: Declare the Speech corresponding to this trainee image as identified Speech.

Step 7: Repeat the Step 5 and Step 6 are repeated for selected portion of feature vector.

Step 8: Return }

3.3.2.5 Convert matrix into Image File To convert matrix A into JPEG formats using MATLAB function called “imwrite”. It stores matrix A in the file path mentioned and also save column matrix D in a new wave file using “wavwrite” function. It clearly describes that JPEG stores the wave file.

3.3.2.6 Convert Image Matrix into Column Matrix The above function is used to save X column vector in the given ‘filename’ with a desired frequency ‘FS’. The column vector X is obtained by converting image matrix of double precision into column matrix.

3.4 PROPOSED ALGORITHM The entire retrieval procedure with the orientation features is presented as simple algorithms hereunder using MATLAB. In order to identify and recognition of feature based images from the databases are followed.

3.4.1 Algorithm – I // Transformation on full image // Begin Step 1: Read an image from the image database (IDB) of size M×N (256 X 256).

Step 2: Calculate Row Mean of an image.

Step 3: Perform procedure training_ feature ( ) Step 3: Perform procedure testing_ feature ( ) Step 4: Repeat Step1 through Step 3 for all the images in IDB. Step 5: Establish feature database set.

End

4. EXPERIMENTATION & RESULTS The experimentation is carried out by MATLAB. It stands for MATrix LABoratory. MATLAB® is a highperformance language for technical computing. It integrates computation, visualization and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation.

To study the proposed approach recorded every Speech 10 occurrences of each sentence were recorded.

Recording was done at varying times. This forms the closed set for our experiment. From these speech samples were created with window size 256 and overlap of 128.

270

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 5. SIMILARITY AND PERFORMANCE MEASURES To find the similarity measures between the images, various metrics are used to measure the distance between features of the images. Some of the well known distance metrics used in for image retrieval is presented below. The Euclidean Distance is calculated as below d E ( x1 , x 2 ) =

Fig.2 Original Wave file

i= n

( x1 ( i ) − x 2 ( i ) ) ∑ i =1

2

… (5.1) Where x1(i) is the feature vector of input image i and x2(i) is the feature vector of the target image i in the image database.

Accuracy The accuracy of the identification system is calculated by No. of matches A (%) = × 100 × 100 -- (5.3) No. of Samples Tested 6. PERFORMANCE EVALUATION The proposed feature extraction is experimented with the images collected from the standard VidTIMIT database and generated feature set images considered for this experiment are of the size. From the below Table 1.1 shows that recognition percentage of query images with Proposed Model gives the higher retrieval accuracy of 86.34%. The performance was evaluated using the Euclidean Distance classification by analysis of the values in the table the Proposed model is better for Speech identification.

Table 1.1 Recognition Accuracy of Full images Portions feature selected

Number of Coefficient

DCT

WALSH

HAAR

Proposed RCF

Fig.3 Code for Wave File into Column Matrix and JPEG format

Fig.3 Wave File into Column Matrix

271

256*256

65536

70.83

70.83

70.83

71.34

192*192

36864

75.27

76.11

77.5

79.56

128*128

16384

78.88

80

80

80.91

64*64

4096

82.77

84.16

84.16

84.82

32*32

1024

87.77

85.55

85.55

86

20*20

400

88.05

84.72

86.39

89.51

16*16

256

87.5

85

85

86.34

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.4 APRIL 2015 Fig.6 Comparison Graph with Existing Model

[9]

[10]

[11] [12] From the above fig.6 shows the pictorial representation of the performance evaluated. By analyzing the obtained results the Proposed RCF produced the best results.

7. CONCLUSION In this paper, the speech recognition and distance based retrieval with feature extraction images based on DCT, WALSH and HAAR models has been presented. The experimental result proves the effectiveness of the proposed RCF methods provides good identification rate and Euclidean distance gives better for recognition of speech when compared to existing methods. The proposed RCF produces better results with 86.34% accuracy compared with existing methods.

[1]

[2]

[3]

[4] [5]

[6]

[7]

[8]

8. REFERENCES S. Davis and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,” IEEE Transaction Acoustics Speech and Signal Processing, vol. 4, pp. 375-366, 1980.

Wang Yutai, Li Bo, Jiang Xiaoqing, Liu Feng, Wang Lihao, “Speaker Recognition Based on Dynamic MFCC Parameters”, International Conference on Image Analysis and Signal Processing, pp. 406-409, 2009.

Azzam Sleit, Sami Serhan, and Loai Nemir, “A histogram based speaker identification technique”, International Conference on ICADIWT, pp. 384-388, 2008.

B. S. Atal, “Automatic Recognition of speakers from their voices”, Proc. IEEE, vol. 64, pp. 460-475, 1976.

Jialong He, Li Liu, and G¨unther Palm, “A discriminative training algorithm for VQ-based speaker Identification”, IEEE Transactions on speech and audio processing, vol.7,No.3, pp. 353-356,1999.

Debadatta Pati, S. R. Mahadeva Prasanna, “NonParametric Vector Quantization of Excitation Source Information for Speaker Recognition”, IEEE Region 10 Conference, pp. 1-4, 2008.

Tridibesh Dutta and Gopal K. Basak, “Text dependent speaker identification using similar patterns in spectrograms”, PRIP'2007 Proceedings, Volume 1, pp.87-92, Minsk, 2007.

Andrew B. Watson, “Image compression using the Discrete Cosine Transform”, Mathematica journal, 4(1), pp. 81-88,1994.

272

Evgeniy Gabrilovich, Alberto D. Berstin: “Speaker recognition: using a vector quantization approach for robust text-independent speaker identification”, Technical report DSPG-95-9-001, 1995.

Tridibesh Dutta, “Text dependent speaker identification based on spectrograms”, Proceedings of Image and vision computing, pp. 238-243, New Zealand 2007.

J.P.Campbell, “Speaker recognition: a tutorial”, Proc.

IEEE, vol. 85, no. 9, pp. 1437-1462, 1997.

D. O.Shaughnessy, “Speech communications- Man and Machine”, New York, IEEE Press, 2nd Ed., pp.

199, pp. 437-458, 2000.

@IJITCE Publication

@IJITCE Publication