James Ethridge

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Two important components of a speaker identification system are the feature extraction and the classification tasks. First, features must be robust to noise and they must also be able to provide discriminating information that the classifier can use to determine the speaker's identity. Second, the classifier must take the features that have been extracted(More)
Using a machine learning algorithm for a given application often requires tuning design parameters of the classifier to obtain optimal classification performance without overfitting. In this contribution, we present an evolutionary algorithm based approach for multi-objective optimization of the sensitivity and specificity of a v-SVM. The v-SVM is often(More)
The performance of a speaker identification (SID) system degrades substantially when there is a mismatch between the training and testing conditions. Discriminating between temporal sections of speech signals which are speech-like (SID usable) and noise-like (SID unusable) while only retaining frames labeled SID usable can augment SID performance(More)
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