Total Variability Layer in Deep Neural Network Embeddings for Speaker Verification

Abstract

The total variability model (TVM) has been extensively used as a tool to obtain a vector representation of the sources of variability present in a signal. However, recent studies have shown that embeddings derived from a deep neural network (DNN) architecture can provide significant performance improvement over TVM for the speaker verification task. In this letter, we show that TVM can also be reformulated in a manner that enables the integration of a DNN within the model. In addition, we show that this TVM architecture can also be incorporated as one of the layers within a DNN embedding system. Through experiments on speakers in the wild (SITW) corpus, we show that the inclusion of total variability layer in a DNN embedding system provides around 20% relative improvement in equal error rate performance.