Learnability of derivationally opaque processes in the Gestural Harmony Model
- Caitlin Smith (Johns Hopkins University)
- Charlie O\'Hara (University of Southern California)
Abstract
In this paper, we examine the learnability of two apparently derivationally opaque vowel harmony patterns: attested stepwise height harmony and unattested saltatory height harmony. We analyze these patterns within the Gestural Harmony Model (Smith, 2018) and introduce a learning algorithm for setting the gestural parameters that generate these harmony patterns. Results of the learning model indicate a learning bias in favor of the attested stepwise pattern and against the unattested saltation pattern, providing a potential explanation for the differences in attestation between these two derivationally opaque patterns.
Keywords: vowel harmony, learnability, derivational opacity, gestures, gestural phonology
How to Cite:
Smith, C. & O\'Hara, C., (2021) “Learnability of derivationally opaque processes in the Gestural Harmony Model”, Society for Computation in Linguistics 4(1), 396-400. doi: https://doi.org/10.7275/4ydq-zb48
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