arnoldysyeung/phonemic-confusion

Predicting phonemic confusion of utterances using deep learning.

Towards Detection and Remediation of Phonemic Confusion

Published in the SIGMORPHON 2021 Workshop at ACL-IJCNLP 2021

The 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology at The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)

Abstract

Reducing communication breakdown is critical to success in interactive NLP applications, such as dialogue systems. To this end, we propose a confusion-mitigation framework for the detection and remediation of communication breakdown. In this work, as a first step towards implementing this framework, we focus on detecting phonemic sources of confusion. As a proof-of-concept, we evaluate two neural architectures in predicting the probability that a listener will misunderstand phonemes in an utterance. We show that both neural models outperform a weighted n-gram baseline, showing early promise for the broader framework.