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Accurate recognition of slot values such as domain specific words or namedentities by automatic speech recognition (ASR) systems forms the core of theGoal-oriented Dialogue Systems. Although it is a critical step with directimpact on downstream tasks such as language understanding, many domain agnosticASR systems tend to perform poorly on domain specific or long tail words. Theyare often supplemented with slot error correcting systems but it is often hardfor any neural model to directly output such rare entity words. To address thisproblem, we propose k-nearest neighbor (k-NN) search that outputsdomain-specific entities from an explicit datastore. We improve errorcorrection rate by conveniently augmenting a pretrained joint phoneme and textbased transformer sequence to sequence model with k-NN search during inference.We evaluate our proposed approach on five different domains containing longtail 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 alsoachieves a relative WER improvement of 9.8% on an out of vocabulary (OOV) testset.

Remember the context! ASR slot error correction through memorization