  In this paper, we apply the NetFV and NetVLAD layers for the end-to-end
language identification task. NetFV and NetVLAD layers are the differentiable
implementations of the standard Fisher Vector and Vector of Locally Aggregated
Descriptors (VLAD) methods, respectively. Both of them can encode a sequence of
feature vectors into a fixed dimensional vector which is very important to
process those variable-length utterances. We first present the relevances and
differences between the classical i-vector and the aforementioned encoding
schemes. Then, we construct a flexible end-to-end framework including a
convolutional neural network (CNN) architecture and an encoding layer (NetFV or
NetVLAD) for the language identification task. Experimental results on the NIST
LRE 2007 close-set task show that the proposed system achieves significant EER
reductions against the conventional i-vector baseline and the CNN temporal
average pooling system, respectively.
