Speaker verification (SV) systems have been shown to be vulnerable to imposture using speech synthesizers. In this paper, we extend previous work in detecting synthetic speech by analyzing words which provide strong discrimination between human and synthetic speech. The research is applicable to authentication systems based on text-dependent SV where the user is prompted to speak a certain utterance which can be chosen by the designer. Our results show that this approach to synthetic speech detection leads to higher accuracies than other proposed approaches. Using various corpora to train and test, our results show 98% accuracy in correctly classifying both human and synthetic speech.
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