  Keyword spotting is a process of finding some specific words or phrases in
recorded speeches by computers. Deep neural network algorithms, as a powerful
engine, can handle this problem if they are trained over an appropriate
dataset. To this end, the football keyword dataset (FKD), as a new keyword
spotting dataset in Persian, is collected with crowdsourcing. This dataset
contains nearly 31000 samples in 18 classes. The continuous speech synthesis
method proposed to made FKD usable in the practical application which works
with continuous speeches. Besides, we proposed a lightweight architecture
called EfficientNet-A0 (absolute zero) by applying the compound scaling method
on EfficientNet-B0 for keyword spotting task. Finally, the proposed
architecture is evaluated with various models. It is realized that
EfficientNet-A0 and Resnet models outperform other models on this dataset.
