Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition. (arXiv:2201.10609v1 [cs.SD])

This work aims to design a low complexity spoken command recognition (SCR)
system by considering different trade-offs between the number of model
parameters and classification accuracy. More specifically, we exploit a deep
hybrid architecture of a tensor-train (TT) network to build an end-to-end SRC
pipeline. Our command recognition system, namely CNN+(TT-DNN), is composed of
convolutional layers at the bottom for spectral feature extraction and TT
layers at the top for command classification. Compared with a traditional
end-to-end CNN baseline for …

arxiv hybrid networks