Segmental Normalization for Robust Speaker Verification
نویسندگان
چکیده
For the task of speaker verification, similarity measure normalization methods are relevant to cope with variability problems and with data and/or decision fusion problems. The aim of this paper is to suggest a new method of normalization which combines classical world model based normalization techniques with ones based on a posteriori probability. This original method presents the well-known advantages of the a posteriori probability based methods without requiring data and speaker specific processing. In this paper, the proposed method is experimented in a framework of a temporal-segmental speaker verification system. The results obtained on a subset of Switchboard-Nist98 database demonstrate the ability of this method to normalize similarity measures (in [0,1] probability domain) without decreasing performances.
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