The weights are estimated on an independent validation set to minimize the overlap between the client and impostors densities. Theoretically, the threshold should be speaker independent when stochastic models are used. However, practical experiments show that this is not the case and, due to modeling assumptions, the threshold actually becomes speaker and utterance length dependent. A theoretical framework showing how to adjust the threshold using the local likelihood ratio is then developed. Finally, a further modeling error correction approach is proposed and tested using decision fusion.
Practical experiments show the advantages and drawbacks of the fusion approach.
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