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This work focuses on the task of speaker recognition under limited data conditions. In case of limited data, the amount of available training and testing data will be few seconds. Under such conditions the conventional classifiers will have very few feature vectors for modelling. This work performs an experimental evaluation of three simple modelling techniques namely, direct template matching (DTM),...
The objective of this work is to demonstrate the feasibility of excitation source information obtained by non-parametric vector quantization (VQ) for speaker recognition task. Linear prediction (LP) residual is used as the representation of excitation source information. The LP residual is subjected to non-parametric VQ during training. The codebooks are built for different codebook sizes. The testing...
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