  Keyword spotting (KWS) is one of the speech recognition tasks most sensitive
to the quality of the feature representation. However, the research on KWS has
traditionally focused on new model topologies, putting little emphasis on other
aspects like feature extraction. This paper investigates the use of the
multitaper technique to create improved features for KWS. The experimental
study is carried out for different test scenarios, windows and parameters,
datasets, and neural networks commonly used in embedded KWS applications.
Experiment results confirm the advantages of using the proposed improved
features.
