  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 SCR, our proposed CNN+(TT-DNN) model replaces fully
connected (FC) layers with TT ones and it can substantially reduce the number
of model parameters while maintaining the baseline performance of the CNN
model. We initialize the CNN+(TT-DNN) model in a randomized manner or based on
a well-trained CNN+DNN, and assess the CNN+(TT-DNN) models on the Google Speech
Command Dataset. Our experimental results show that the proposed CNN+(TT-DNN)
model attains a competitive accuracy of 96.31% with 4 times fewer model
parameters than the CNN model. Furthermore, the CNN+(TT-DNN) model can obtain a
97.2% accuracy when the number of parameters is increased.
