Towards Detection and Remediation of Phonemic Confusion

Francois Roewer-Despres, Arnold Yeung, Ilan Kogan


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.
Anthology ID:
2021.sigmorphon-1.1
Volume:
Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
Month:
August
Year:
2021
Address:
Online
Editors:
Garrett Nicolai, Kyle Gorman, Ryan Cotterell
Venue:
SIGMORPHON
SIG:
SIGMORPHON
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/2021.sigmorphon-1.1
DOI:
10.18653/v1/2021.sigmorphon-1.1
Bibkey:
Cite (ACL):
Francois Roewer-Despres, Arnold Yeung, and Ilan Kogan. 2021. Towards Detection and Remediation of Phonemic Confusion. In Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 1–10, Online. Association for Computational Linguistics.
Cite (Informal):
Towards Detection and Remediation of Phonemic Confusion (Roewer-Despres et al., SIGMORPHON 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.sigmorphon-1.1.pdf
Video:
 https://aclanthology.org/2021.sigmorphon-1.1.mp4
Code
 francois-rd/phonemic-confusion