Voice based biometric system using
Matlab GUI:
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.
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