Eric Meinhardt


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Speakers enhance contextually confusable words
Eric Meinhardt | Eric Bakovic | Leon Bergen
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent work has found evidence that natural languages are shaped by pressures for efficient communication — e.g. the more contextually predictable a word is, the fewer speech sounds or syllables it has (Piantadosi et al. 2011). Research on the degree to which speech and language are shaped by pressures for effective communication — robustness in the face of noise and uncertainty — has been more equivocal. We develop a measure of contextual confusability during word recognition based on psychoacoustic data. Applying this measure to naturalistic speech corpora, we find evidence suggesting that speakers alter their productions to make contextually more confusable words easier to understand.

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Questioning to Resolve Transduction Problems
Eric Meinhardt | Anna Mai | Eric Baković | Adam McCollum
Proceedings of the Society for Computation in Linguistics 2020


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Constraint-based Learning of Phonological Processes
Shraddha Barke | Rose Kunkel | Nadia Polikarpova | Eric Meinhardt | Eric Bakovic | Leon Bergen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Phonological processes are context-dependent sound changes in natural languages. We present an unsupervised approach to learning human-readable descriptions of phonological processes from collections of related utterances. Our approach builds upon a technique from the programming languages community called *constraint-based program synthesis*. We contribute a novel encoding of the learning problem into Boolean Satisfiability constraints, which enables both data efficiency and fast inference. We evaluate our system on textbook phonology problems and datasets from the literature, and show that it achieves high accuracy at interactive speeds.