Remember the context! ASR slot error correction through memorization
This addresses slot error correction for goal-oriented dialogue systems, offering incremental improvements in handling rare entities.
The paper tackled the problem of poor ASR performance on domain-specific or rare slot values by proposing a k-nearest neighbor search method to correct errors, achieving relative WER improvements of 7.4% on rare words and 9.8% on out-of-vocabulary test sets.
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.