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Efficient text-independent speaker verification with structural gaussian mixture models and neural network
IEEE Transactions on Speech and Audio Processing
We present an integrated system with structural Gaussian mixture models (SGMMs) and a neural network for purposes of achieving both computational efficiency and high accuracy in text-independent speaker verification. A structural background model (SBM) is constructed first by hierarchically clustering all Gaussian mixture components in a universal background model (UBM). In this way the acoustic space is partitioned into multiple regions in different levels of resolution. For each targetdoi:10.1109/tsa.2003.815822 fatcat:hvrmfl45vrehpfm7pi4quykry4