TY - JOUR
T1 - The Presence of Background Noise Extends the Competitor Space in Native and Non-Native Spoken-Word Recognition
T2 - Insights from Computational Modeling
AU - Karaminis, Themis
AU - Hintz, Florian
AU - Scharenborg, Odette
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Competitor space
KW - Computational modeling
KW - Deep neural networks
KW - Neurocomputational model
KW - Noise
KW - Non-native listening
KW - Phonological competition
KW - Spoken-word recognition
UR - http://www.scopus.com/inward/record.url?scp=85125002844&partnerID=8YFLogxK
U2 - 10.1111/cogs.13110
DO - 10.1111/cogs.13110
M3 - Article
C2 - 35188686
AN - SCOPUS:85125002844
SN - 0364-0213
VL - 46
JO - Cognitive Science
JF - Cognitive Science
IS - 2
M1 - e13110
ER -