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Voice Based Biometric System

3.8 | 4 ratings Rate this file 31 Downloads (last 30 days) File Size: 1.19 MB File ID: #31328 Version: 1.0

Voice Based Biometric System


ambavi patel (view profile)


Biometric property speech used for text dependent speaker recognition system.

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Biometrics is an emerging field of technology using unique and measurable physical, biological, or behavioral characteristics that can be processed to identify a person. Biometric properties of human are fingerprint, iris, face and voice.
  A concise definition of biometrics is “the automatic recognition of person using distinguishing traits.”
One of the biometric property, Speech is produced as a sequence of sounds. The vibration of the vocal cords, as well as the positions, shapes, and sizes of the various articulators (such as the tongue, lips, and teeth) generate the sound being produced .The characteristics of the sound vary from person to person and can be used to identify an individual. Although typically not considered as accurate as other types of biometric identification systems, a voice recognition system can be used in conjunction with other biometric systems to create a more robust recognition system
Speaker Recognition mainly involves two modules namely feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the speaker’s voice signal that can later be used to represent that speaker. Feature matching involves the actual procedure to identify the unknown speaker by comparing the extracted features from his/her voice input with the ones that are already stored in our speech database.
In feature extraction we find the Mel Frequency Cepstrum Coefficients (MFCC), which are based on the known variation of the human ear’s critical bandwidths with frequency and Centroid from MFCC matrix results in the speaker specific database.
In feature matching we used two methods .In first method centroids from row and column are found and codebook is made. For recognition the minimum Euclidian distance between the input utterance of an unknown speaker and the stored database should less than threshold value.
In second method using neural network toolbox,back propagation network created. for recognition Regression between output of network and desired output should higher than threshold value.

Required Products Neural Network Toolbox
MATLAB release MATLAB 7.8 (R2009a)
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Comments and Ratings (8)
01 May 2014 Toan Phuong

great code. it's very usefull. Please Give me the details or related papers of this. Thank you very much

30 May 2013 Yo

Yo (view profile)

13 Mar 2013 Milos Denic

30 Nov 2012 Sara Hafeez

nice code

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09 Sep 2012 abhi

abhi (view profile)

19 Nov 2011 Rajan Miglani

Dear Sir, I'm new to using matlab. will you please let me know to train this neural netwrok..I badly need to understand this code. hope u'l help me in time... thxxx

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13 Oct 2011 thirupathi moorthy

please give me related papers to this coding for my understanding

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08 Aug 2011 Anzar

Anzar (view profile)

Please Give the details of Training and testing using Neural network

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