Reinforced Temporal Structure Information For Embedded Utterance-Based Speaker Recognition

Abstract : Embedded speaker recognition in mobile devices could involve several ergonomic constraints and a limited amount of computing resources. Even if they have proved their efficiency in more classical contexts, GMM/UBM based systems show their limits in such situations, with good accuracy demanding a relatively large quantity of speech data, but with negligible harnessing of linguistic content. The proposed approach addresses these limitations and takes advantage from the linguistic nature of the speech material into the GMM/UBM framework by using client-customised utterances. The GMM/UBM is then reinforced with new temporal information. Experiments on the MyIdea database are performed when impostors know the client-utterance and also when they do not, highlighting the potential of this new approach. A relative gain up to 45% in terms of EER is achieved when impostors do not know the client utterance and performance is equivalent to the GMM/UBM baseline system in other configurations.
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Anthony Larcher, Jean-François Bonastre, John Mason. Reinforced Temporal Structure Information For Embedded Utterance-Based Speaker Recognition. Interspeech, Sep 2008, brisbane, Australia. ⟨hal-01312944⟩

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