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Model quality evaluation during enrolment for speaker verification

The amount of data usually determines the robustness of speaker models in speaker recognition. In this sense, it is convenient to set a model quality measure for every speaker and to classify models into different categories according to their quality level. We propose a new quality measure, which uses only data from clients, based on the number of training utterances that surpasses a certain threshold. If the desired quality is not high enough, the quality measure allows for the detection of non-representative utterances. Once selected, these utterances, considered as outliers, can be removed or better replaced by new ones coming from the same speaker. A database of 184 speakers in Spanish is used to obtain empirical results with connected digits. Our experiments removing outliers and replacing them by new utterances coming from the same speaker outperform the baseline experiments by 40%.