Abstract:
Voice is both physiological and behavioral trait of a human. Hence, the voice based identification
system is convenient for both on-site and remote ways. Despite having some important aspects, the
speaker based identification system is one of the most challenging fields in the biometric-based
authentication system. These challenges occur especially due to environmental or channel based
noise, lack of training samples, and the difference between training and testing environment.
However, different noise elimination techniques stand for the different type of noises. And, finding
the noise type from a voice signal is not an easy task. This research has focused on two important
aspects of these challenges: firstly, high energetic portion of a voice signal has been focused, as
high energy portion of signal is less contaminated by noise, and secondly, speaker identification has
been carried out with less trained data for the scenario where multi conditional training data is not
available. To achieve this aim, the high energetic signal portion has been selected to extract voice
features and hence Linear Prediction has been applied to the selected portion of dynamic model
orders. Dynamic Model Order (DMO) approach is determined by the number of local maxima of
the selected signal portions which are followed by the feature extraction step is performed using
Cepstral Coefficient (CC) analysis. This approach shows a significant accuracy result than the stateof-
art technology.
