Optimizing Voice Recognition using Various Techniques
Voice recognition is a process of recognizing a person
on the basis of their speech sample. This paper describes various techniques that are used for voice recognition in order to optimize the recognition rate. The different techniques that are described in this paper are Linear Predictive Coding (LPC), Neural Networks (NN),
Mel Frequency Cepstrum Coefficients (MFCC), Vector quantization (VQ), Euclidean Distance. MFCC and LPC are used to extract speaker specific characteristics from voice signal. Neural Networks and
Euclidean Distance are used for matching the characteristics extracted using MFCC and LPC. The recognition rates are calculated in each method and they are compared. Mel Frequency Cepstrum Coefficients
gives better recognition rate when compared with the other two
techniques. Various other approaches for implementing voice
recognition are Hidden Markov Modeling (HMM), Gaussian Mixture Modeling (GMM), and Dynamic Time Warping etc. The Voice Recognition system has potential applications in various fields. Some of them are access control to computers, telephone banking, forensics, speech recognition etc.
Douglas A. Reynolds ”An Overview of Automatic Speaker Recognition
D. B. Hanchate, Mohini Nalawade, Manoj Pawar, Vijay Pophale, Prabhat
Kumar Maurya”Vocal Digit Recognition using Artificial Neural
Thiang and Suryo Wijoyo “Speech Recognition Using Linear Predictive
Coding and Artificial Neural Network for Controlling Movement of
Mobile Robot” 2011 International Conference on Information and
Electronics Engineering, IPCSIT vol.6(2011),(2011)IACSIT,Singapore.
Khalid T. Al-Sarayreh, Rafa E. Al-Qutaish et. Al. “Using the Sound
Recognition Techniques to Reduce the Electricity Consumption in
Highways”, Journal of American Science 2009.
Ganesh K Venayagamoorthy, Viresh Moonasar and Kumbes
Sandrasegaran “Voice Recognition Using Neural Networks”, IEEE,
Sangeeta Biswas, Shamim Ahmad and Md. Khademul Islam Molla
“Speaker Identification Using Cepstral Based Features And Discrete
Hidden Markov Model” International Conference on Information and
Communication Technology, ICICT 2007.
H.B.Kekre, Sudeep D. Thepade et. Al.” Image Retrieval Using Texture
Features Extracted Using Lbg, Kpe, Kfcg, Kmcg, Kevr With Assorted
Color Spaces” International Journal of Advances in Engineering and
Technology, Jan 2012.
Satyanand Singh, Dr. E .G. Rajan “MFCC VQ Based Speaker
Recognition and its Accuracy Affecting Factos”,International Journal of
Computer Applications ,2011.
Ahsanul Kabir, Sheikh Mohammad Masudul Ahsan “Vector
Quantization In Text Dependent Automatic Speaker Recognition Using
Mel-frequency Cepstrum Coefficient” 6th WSEAS International
Conference on CIRCUITS, SYSTEMS, ELECTRONICS,CONTROL &
SIGNAL PROCESSING, Cairo, Egypt, Dec 29-31, 2007 352.
H B Kekre, Vaishali Kulkarni, “Speaker Identification by using Vector
Quantization”, International Journal of Engineering Science and
Technology, May 2010 edition.
H. B. Kekre, Tanuja K. Sarode, “An Efficient Fast Algorithm to Generate
Codebook for Vector Quantization,” First International Conference on
Emerging Trends in Engineering and Technology, ICETET-2008, held at
Raisoni College of Engineering, Nagpur, India, 16-18 July 2008,
Avaliable at online IEEE Xplore.
Y. Linde, A. Buzo, and R. M. Gray.: „An algorithm for vector quantizer
design,” IEEE Trans.Commun.‟, vol. COM-28, no. 1, pp. 84-95, 1980.
Md. Rashidul Hasan, Mustafa Jamil, Md. Golam Rabbani, Md. Saifur
Rahman “Speaker Identification Using Mel Frequency Cepstral
Coefficients”,3rd International Conference on Electrical and Computer
Engineering, ICECE 2004.
Vibha Tiwari “MFCC and its Applications in Speaker Recognition”
International Journal on Emerging Technologies, 2010
N. Botros, M.Z. Deiri and.P. Hsu “Automatic Voice Recognition Using
Artificial Neural Network Approach”,IEEE,1990.
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