Web: http://arxiv.org/abs/2201.10609

Jan. 27, 2022, 2:10 a.m. | Jun Qi, Javier Tejedor

cs.LG updates on arXiv.org arxiv.org

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 models networks

Engineering Manager, Machine Learning (Credit Engineering)

@ Affirm | Remote Poland

Sr Data Engineer

@ Rappi | [CO] Bogotá

Senior Analytics Engineer

@ GetGround | Porto

Senior Staff Software Engineer, Data Engineering

@ Galileo, Inc. | New York City or Remote

Data Engineer

@ Atlassian | Bengaluru, India

Data Engineer | Hybrid (Pune)

@ Velotio | Pune, Maharashtra, India