Stacked bottleneck features for speaker verification

i-Vector modeling has shown to be effective for text independent speaker verification. It represents each utterance as a low-dimensional vector using factor analysis with a GMM supervector. In order to capture more complex speaker statistics, this paper proposes a new feature representation other than i-vectors for speaker verification using neural networks. In this work, stacked bottleneck features are extracted from cascade neural networks based on GMM supervectors. Dropout is integrated into the model to improve generalization error. We compare the proposed method with i-vector approach on NIST SRE2008 female short2-short3 telephone-telephone task. Experimental results demonstrate the efficacy of the proposed method.