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There is immense potential for speaker verification system under limited data condition in several real life applications. This paper explains how the combined dissimilar characteristics of voice data improve the performance of speaker verification when training and testing data lengths are reduced (less than 15 sec). To carry out this work, Mel-Frequency Cepstral Coefficients (MFCC), Linear Prediction...
The present work explains the speaker verification under limited data conditions. In real time situation such as forensic investigation the amount of speech data available for training and test may be less in terms of few seconds. Recognizing culprits in such condition is a challenging task as the available feature vectors are less in numbers. To increase the performance of speaker verification under...
In this paper, the task of verifying the speaker using limited training and testing data is addressed. Speaker verification under limited data may not be able to produce sufficient feature vector for training and testing. This creates poor speaker modelling during training and testing. To defeat this problem, feature vectors for training and testing are increased. To increase the feature vectors,...
This work address text-independent speaker verification with the constraint of limited data (<15 seconds). The existing techniques for speaker verification work well for sufficient data (>1 minute). Developing techniques for verifying the speakers for limited data condition is a challenging issue since data available of speakers is very small nowadays. This is because people reluctant to give...
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