  We propose a new method of generating meaningful embeddings for speech,
changes to four commonly used meta learning approaches to enable them to
perform keyword spotting in continuous signals and an approach of combining
their outcomes into an end-to-end automatic speech recognition system to
improve rare word recognition. We verify the functionality of each of our three
contributions in two experiments exploring their performance for different
amounts of classes (N-way) and examples per class (k-shot) in a few-shot
setting. We find that the speech embeddings work well and the changes to the
meta learning approaches also clearly enable them to perform continuous signal
spotting. Despite the interface between keyword spotting and speech recognition
being very simple, we are able to consistently improve word error rate by up to
5%.
