  Neural sequence-to-sequence systems deliver state-of-the-art performance for
automatic speech recognition (ASR). When using appropriate modeling units,
e.g., byte-pair encoded characters, these systems are in principal open
vocabulary systems. In practice, however, they often fail to recognize words
not seen during training, e.g., named entities, numbers or technical terms. To
alleviate this problem we supplement an end-to-end ASR system with a
word/phrase memory and a mechanism to access this memory to recognize the words
and phrases correctly. After the training of the ASR system, and when it has
already been deployed, a relevant word can be added or subtracted instantly
without the need for further training. In this paper we demonstrate that
through this mechanism our system is able to recognize more than 85% of newly
added words that it previously failed to recognize compared to a strong
baseline.
