  We use dynamic time warping (DTW) as supervision for training a convolutional
neural network (CNN) based keyword spotting system using a small set of spoken
isolated keywords. The aim is to allow rapid deployment of a keyword spotting
system in a new language to support urgent United Nations (UN) relief
programmes in parts of Africa where languages are extremely under-resourced and
the development of annotated speech resources is infeasible. First, we use 1920
recorded keywords (40 keyword types, 34 minutes of speech) as exemplars in a
DTW-based template matching system and apply it to untranscribed broadcast
speech. Then, we use the resulting DTW scores as targets to train a CNN on the
same unlabelled speech. In this way we use just 34 minutes of labelled speech,
but leverage a large amount of unlabelled data for training. While the
resulting CNN keyword spotter cannot match the performance of the DTW-based
system, it substantially outperforms a CNN classifier trained only on the
keywords, improving the area under the ROC curve from 0.54 to 0.64. Because our
CNN system is several orders of magnitude faster at runtime than the DTW
system, it represents the most viable keyword spotter on this extremely limited
dataset.
