  The currently most prominent algorithm to train keyword spotting (KWS) models
with deep neural networks (DNNs) requires strong supervision i.e., precise
knowledge of the spoken keyword location in time. Thus, most KWS approaches
treat the presence of redundant data, such as noise, within their training set
as an obstacle. A common training paradigm to deal with data redundancies is to
use temporally weakly supervised learning, which only requires providing labels
on a coarse scale. This study explores the limits of DNN training using
temporally weak labeling with applications in KWS. We train a simple end-to-end
classifier on the common Google Speech Commands dataset with increased
difficulty by randomly appending and adding noise to the training dataset. Our
results indicate that temporally weak labeling can achieve comparable results
to strongly supervised baselines while having a less stringent labeling
requirement. In the presence of noise, weakly supervised models are capable to
localize and extract target keywords without explicit supervision, leading to a
performance increase compared to strongly supervised approaches.
