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We propose a new method of generating meaningful embeddings for speech,changes to four commonly used meta learning approaches to enable them toperform keyword spotting in continuous signals and an approach of combiningtheir outcomes into an end-to-end automatic speech recognition system toimprove rare word recognition. We verify the functionality of each of our threecontributions in two experiments exploring their performance for differentamounts of classes (N-way) and examples per class (k-shot) in a few-shotsetting. We find that the speech embeddings work well and the changes to themeta learning approaches also clearly enable them to perform continuous signalspotting. Despite the interface between keyword spotting and speech recognitionbeing very simple, we are able to consistently improve word error rate by up to5%.

Meta-Learning for improving rare word recognition in end-to-end ASR