6 citations · 6 across the 3 of their papers we have counts for
7 papers
Estimating Surprisal from the Speech Signal
Zachary Hopton, Samuel Kiegeland, Francesco Ignazio Re +4
Although language is predominantly spoken, empirical tests of surprisal theory rely almost entirely on written text. In practice, surprisal is estimated using language models (LMs)…
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics
Zachary Hopton, Francesco Ignazio Re, Samuel Kiegeland +3
Contextual and unigram surprisal are standard predictors of reading time in computational psycholinguistics. While contextual surprisal is typically derived from neural language mo…
DeepFry: Identifying Vocal Fry Using Deep Neural Networks
Bronya R. Chernyak, Talia Ben Simon, Yael Segal +4
Vocal fry or creaky voice refers to a voice quality characterized by irregular glottal opening and low pitch. It occurs in diverse languages and is prevalent in American English, w…
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova, Jennifer White, Elizabeth Salesky +25
A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from…
A Corpus for Large-Scale Phonetic Typology
Elizabeth Salesky, Eleanor Chodroff, Tiago Pimentel +4
A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions. We present VoxClamantis v1.0, the first large-scale c…
Predicting Declension Class from Form and Meaning
Adina Williams, Tiago Pimentel, Arya D. McCarthy +3
The noun lexica of many natural languages are divided into several declension classes with characteristic morphological properties. Class membership is far from deterministic, but…