  In this paper, we propose an innovative approach to perform speaker
recognition by fusing two recently introduced deep neural networks (DNNs)
namely - SincNet and X-Vector. The idea behind using SincNet filters on the raw
speech waveform is to extract more distinguishing frequency-related features in
the initial convolution layers of the CNN architecture. X-Vectors are used to
take advantage of the fact that this embedding is an efficient method to churn
out fixed dimension features from variable length speech utterances, something
which is challenging in plain CNN techniques, making it efficient both in terms
of speed and accuracy. Our approach uses the best of both worlds by combining
X-vector in the later layers while using SincNet filters in the initial layers
of our deep model. This approach allows the network to learn better embedding
and converge quicker. Previous works use either X-Vector or SincNet Filters or
some modifications, however we introduce a novel fusion architecture wherein we
have combined both the techniques to gather more information about the speech
signal hence, giving us better results. Our method focuses on the VoxCeleb1
dataset for speaker recognition, and we have used it for both training and
testing purposes.
