Abstract
Speaker identification is a biometric technique. The
objective of automatic speaker recognition is to extract,
characterize and recognize the information about speaker
identity. Speaker Recognition technology has recently been
used in large number of commercial areas successfully such as
in voice based biometrics; voice controlled appliances, security
control for confidential information, remote access to
computers and many more interesting areas. A speaker
identification system has two phases which are the training
phase and the testing phase. Feature extraction is the first step
for each phase in speaker recognition. Many algorithms are
suggested by the researchers for feature extraction. In this
work, the Mel Frequency Cepstrum Coefficient (MFCC)
feature has been used for designing a text dependent speaker
identification system. While, in the identification phase, the
existing reference templates are compared with the unknown
voice input. In this thesis, a Bayesian network is used as the
training/recognition algorithm which makes the final decision
about the specification of the speaker by comparing unknown
features to all models in the database and selecting the best
matching model. i, e. the highest scored model. The speaker
who obtains the highest score is selected as the target speaker.
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