Abstract
The National Institute of Standards and Technology (NIST) 2012 speaker recognition evaluation posed several new challenges including noisy data, varying test-sample length and number of enrollment samples, and a new metric. Target speakers were known during system development and could be used for model training and score normalization. For the evaluation, SRI International (SRI) submitted a system consisting of six subsystems that use different low- and high-level features, some specifically designed for noise robustness, fused at the score and iVector levels. This paper presents SRI’s submission along with a careful analysis of the approaches that provided gains for this challenging evaluation including a multiclass voice-activity detection system, the use of noisy data in system training, and the fusion of subsystems using acoustic characterization metadata. Index Terms: Speaker recognition, noise-robustness, PLDA, iVector
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