Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
This paper presents an attempt at assessing empirically how a state-of-the-art text-independent speaker verification system behaves when confronted to imposting attempts from a professional imitator who perfectly knows how to imitate in particular the clients he tried to impost. Empirical evidence show that, fortunately, current speaker verification systems are indeed robust to such attempts, even when humans are not able to discriminate between true and imposting accesses (a website with some examples is provided to convince the reader). Furthermore, we show that the knowledge of the lexical content of the access significantly helps the imitator, although fortunately not enough to fool the system. This study thus represents a first step in assessing a speaker verification system against true, informed, impostors.