The Presence of Background Noise Extends the Competitor Space in Native and Non-Native Spoken-Word Recognition: Insights from Computational Modeling.

Publisher: Wiley-Blackwell Country of Publication: United States NLM ID: 7708195 Publication Model: Print Cited Medium: Internet ISSN: 1551-6709 (Electronic) Linking ISSN: 03640213 NLM ISO Abbreviation: Cogn Sci Subsets: MEDLINE

Publication: 2009-: Hoboken, N.J. : Wiley-Blackwell
Original Publication: Norwood, N. J., Ablex Pub. Corp.

Speech Perception*/physiology; Computer Simulation ; Humans ; Language ; Noise ; Recognition, Psychology/physiology

Oral communication often takes place in noisy environments, which challenge spoken-word recognition. Previous research has suggested that the presence of background noise extends the number of candidate words competing with the target word for recognition and that this extension affects the time course and accuracy of spoken-word recognition. In this study, we further investigated the temporal dynamics of competition processes in the presence of background noise, and how these vary in listeners with different language proficiency (i.e., native and non-native) using computational modeling. We developed ListenIN (Listen-In-Noise), a neural-network model based on an autoencoder architecture, which learns to map phonological forms onto meanings in two languages and simulates native and non-native spoken-word comprehension. We also examined the model's activation states during online spoken-word recognition. These analyses demonstrated that the presence of background noise increases the number of competitor words, which are engaged in phonological competition and that this happens in similar ways intra and interlinguistically and in native and non-native listening. Taken together, our results support accounts positing a "many-additional-competitors scenario" for the effects of noise on spoken-word recognition.
(© 2022 The Authors. Cognitive Science published by Wiley Periodicals LLC on behalf of Cognitive Science Society (CSS).)

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Keywords: Competitor space; Computational modeling; Deep neural networks; Neurocomputational model; Noise; Non-native listening; Phonological competition; Spoken-word recognition

Date Created: 20220221 Date Completed: 20220401 Latest Revision: 20220731

20231215

PMC9286693

10.1111/cogs.13110

35188686