Clustering similar acoustic classes in the Fishervoice framework

Abstract

In the Fishervoice (FSH) based framework, the mean supervectors of the speaker models are divided into several subvectors by mixture index. However, this division strategy cannot capture local acoustic class structure information among similar acoustic classes or discriminative information between different acoustic classes. In order to verify whether or not local structure information can help improve system performance, we develop five different speaker supervector segmentation methods. Experiments on NIST SRE08 prove that clustering similar acoustic classes together improves the system performance. In particular, the proposed method of equal size clustering achieves 5.1% relative decrease on EER compared to FSH1.