  Accurate recognition of slot values such as domain specific words or named
entities by automatic speech recognition (ASR) systems forms the core of the
Goal-oriented Dialogue Systems. Although it is a critical step with direct
impact on downstream tasks such as language understanding, many domain agnostic
ASR systems tend to perform poorly on domain specific or long tail words. They
are often supplemented with slot error correcting systems but it is often hard
for any neural model to directly output such rare entity words. To address this
problem, we propose k-nearest neighbor (k-NN) search that outputs
domain-specific entities from an explicit datastore. We improve error
correction rate by conveniently augmenting a pretrained joint phoneme and text
based transformer sequence to sequence model with k-NN search during inference.
We evaluate our proposed approach on five different domains containing long
tail slot entities such as full names, airports, street names, cities, states.
Our best performing error correction model shows a relative improvement of 7.4%
in word error rate (WER) on rare word entities over the baseline and also
achieves a relative WER improvement of 9.8% on an out of vocabulary (OOV) test
set.
