Naomi Feldman

Also published as: Naomi H. Feldman


A phonetic model of non-native spoken word processing
Yevgen Matusevych | Herman Kamper | Thomas Schatz | Naomi Feldman | Sharon Goldwater
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Non-native speakers show difficulties with spoken word processing. Many studies attribute these difficulties to imprecise phonological encoding of words in the lexical memory. We test an alternative hypothesis: that some of these difficulties can arise from the non-native speakers’ phonetic perception. We train a computational model of phonetic learning, which has no access to phonology, on either one or two languages. We first show that the model exhibits predictable behaviors on phone-level and word-level discrimination tasks. We then test the model on a spoken word processing task, showing that phonology may not be necessary to explain some of the word processing effects observed in non-native speakers. We run an additional analysis of the model’s lexical representation space, showing that the two training languages are not fully separated in that space, similarly to the languages of a bilingual human speaker.


Modeling the learning of the Person Case Constraint
Adam Liter | Naomi Feldman
Proceedings of the Society for Computation in Linguistics 2020


Evaluating Low-Level Speech Features Against Human Perceptual Data
Caitlin Richter | Naomi H. Feldman | Harini Salgado | Aren Jansen
Transactions of the Association for Computational Linguistics, Volume 5

We introduce a method for measuring the correspondence between low-level speech features and human perception, using a cognitive model of speech perception implemented directly on speech recordings. We evaluate two speaker normalization techniques using this method and find that in both cases, speech features that are normalized across speakers predict human data better than unnormalized speech features, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a new framework for evaluating low-level representations of speech on their match to human perception, and lays the groundwork for creating more ecologically valid models of speech perception.

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Learning an Input Filter for Argument Structure Acquisition
Laurel Perkins | Naomi Feldman | Jeffrey Lidz
Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)

How do children learn a verb’s argument structure when their input contains nonbasic clauses that obscure verb transitivity? Here we present a new model that infers verb transitivity by learning to filter out non-basic clauses that were likely parsed in error. In simulations with child-directed speech, we show that this model accurately categorizes the majority of 50 frequent transitive, intransitive and alternating verbs, and jointly learns appropriate parameters for filtering parsing errors. Our model is thus able to filter out problematic data for verb learning without knowing in advance which data need to be filtered.

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Rational Distortions of Learners’ Linguistic Input
Naomi Feldman
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Language acquisition can be modeled as a statistical inference problem: children use sentences and sounds in their input to infer linguistic structure. However, in many cases, children learn from data whose statistical structure is distorted relative to the language they are learning. Such distortions can arise either in the input itself, or as a result of children’s immature strategies for encoding their input. This work examines several cases in which the statistical structure of children’s input differs from the language being learned. Analyses show that these distortions of the input can be accounted for with a statistical learning framework by carefully considering the inference problems that learners solve during language acquisition


Joint Word Segmentation and Phonetic Category Induction
Micha Elsner | Stephanie Antetomaso | Naomi Feldman
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)


Why discourse affects speakers’ choice of referring expressions
Naho Orita | Eliana Vornov | Naomi Feldman | Hal Daumé III
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


Quantifying the role of discourse topicality in speakers’ choices of referring expressions
Naho Orita | Naomi Feldman | Jordan Boyd-Graber | Eliana Vornov
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics

Weak semantic context helps phonetic learning in a model of infant language acquisition
Stella Frank | Naomi H. Feldman | Sharon Goldwater
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability
Micha Elsner | Sharon Goldwater | Naomi Feldman | Frank Wood
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing