|Pierre-Michel Bousquet, Mickaël Rouvier|
This paper investigates the domain adaptation back-end methods introduced over the past years for speaker recognition, when the mismatch between training and test data induces a severe degradation of performance. This analyses lead to suggest some ways, experimentally validated, for the task of collecting in-domain data. The proposed strategy helps to quickly increase accuracy of the detection, without omitting to take into account the practical difficulties of the task of data collecting in real-life situations and without the delay for forming the expected large and speaker-labeled in-domain dataset. Moreover, a new approach of artificial speaker labeling by clustering is proposed, that dispenses of forming a preliminary annotated in-domain dataset, with a similar gain of efficiency.