  Trigger-word detection plays an important role as the entry point of user's
communication with voice assistants. But supporting a particular word as a
trigger-word involves huge amount of data collection, augmentation and
labelling for that word. This makes supporting new trigger-words a tedious and
time consuming process. To combat this, we explore the use of contrastive
learning as a pre-training task that helps the detection model to generalize to
different words and noise conditions. We explore supervised contrastive
techniques and also propose a novel self-supervised training technique using
chunked words from long sentence audios. We show that both supervised and the
new self-supervised contrastive pre-training techniques have comparable results
to a traditional classification pre-training on new trigger words with less
data availability.
