Improving Short Utterance based I-vector Speaker Recognition using Source and Utterance-Duration Normalization Techniques

A significant amount of speech is typically required for speaker verification system development and evaluation, especially in the presence of large intersession variability. This paper in-troduces a source and utterance-duration normalized linear dis-criminant analysis (SUN-LDA) approaches to compensate ses-sion variability in short-utterance i-vector speaker verification systems. Two variations of SUN-LDA are proposed where normalization techniques are used to capture source variation from both short and full-length development i-vectors, one based upon pooling (SUN-LDA-pooled) and the other on con-catenation (SUN-LDA-concat) across the duration and source-dependent session variation. Both the SUN-LDA-pooled and SUN-LDA-concat techniques are shown to provide improve-ment over traditional LDA on NIST 08 truncated 10sec-10sec evaluation conditions, with the highest improvement obtained with the SUN-LDA-concat technique achieving a relative im-provement of 8 % in EER for mis-matched conditions and over 3 % for matched conditions over traditional LDA approaches. Index Terms: speaker verification, i-vector, total-variability, LDA, WCC