  Many deep learning applications, like keyword spotting, require the
incorporation of new concepts (classes) over time, referred to as Class
Incremental Learning (CIL). The major challenge in CIL is catastrophic
forgetting, i.e., preserving as much of the old knowledge as possible while
learning new tasks. Various techniques, such as regularization, knowledge
distillation, and the use of exemplars, have been proposed to resolve this
issue. However, prior works primarily focus on the incremental learning step,
while ignoring the optimization during the base model training. We hypothesize
that a more transferable and generalizable feature representation from the base
model would be beneficial to incremental learning.
  In this work, we adopt multitask learning during base model training to
improve the feature generalizability. Specifically, instead of training a
single model with all the base classes, we decompose the base classes into
multiple subsets and regard each of them as a task. These tasks are trained
concurrently and a shared feature extractor is obtained for incremental
learning. We evaluate our approach on two datasets under various
configurations. The results show that our approach enhances the average
incremental learning accuracy by up to 5.5%, which enables more reliable and
accurate keyword spotting over time. Moreover, the proposed approach can be
combined with many existing techniques and provides additional performance
gain.
