  Deep learning (DL) has brought about remarkable breakthrough in processing
images, video and speech due to its efficacy in extracting highly abstract
representation and learning very complex functions. However, there is seldom
operating procedure reported on how to make it for real use cases. In this
paper, we intend to address this problem by presenting a generalized operating
procedure for DL from the perspective of unconstrained optimal design, which is
motivated by a simple intension to remove the barrier of using DL, especially
for those scientists or engineers who are new but eager to use it. Our proposed
procedure contains seven steps, which are project/problem statement, data
collection, architecture design, initialization of parameters, defining loss
function, computing optimal parameters, and inference, respectively. Following
this procedure, we build a multi-stream end-to-end speaker verification system,
in which the input speech utterance is processed by multiple parallel streams
within different frequency range, so that the acoustic modeling can be more
robust resulting from the diversity of features. Trained with VoxCeleb dataset,
our experimental results verify the effectiveness of our proposed operating
procedure, and also show that our multi-stream framework outperforms
single-stream baseline with 20 % relative reduction in minimum decision cost
function (minDCF).
