TY - GEN
T1 - The Effects of Background Noise on Native and Non-native Spoken-word Recognition
T2 - 40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
AU - Karaminis, Themis
AU - Scharenborg, Odette
N1 - Funding Information:
This research was funded by a Vidi-grant from the Netherlands Organization for Scientific Research (NWO; grant number: 276--89--003) awarded to OS. We would like to thank Jiska Koemans for her help with the phonetic transcriptions. Thanks also to Aditi Lahiri, Caroline Floccia, Kim Plunkett and Jeremy Goslin for their contributions in an earlier version of this model.
Publisher Copyright:
© 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.
PY - 2018/7/28
Y1 - 2018/7/28
N2 - How does the presence of background noise affect the cognitive processes underlying spoken-word recognition? And how do these effects differ in native and non-native language listeners? We addressed these questions using artificial neural-network modelling. We trained a deep auto-encoder architecture on binary phonological and semantic representations of 121 English and Dutch translation equivalents. We also varied exposure to the two languages to generate ‘native English’ and ‘non-native English’ trained networks. These networks captured key effects in the performance (accuracy rates and the number of erroneous responses per word stimulus) of English and Dutch listeners in an offline English spoken-word identification experiment (Scharenborg et al., 2017), which considered clean and noisy listening conditions and three intensities of speech-shaped noise, applied word-initially or word-finally. Our simulations suggested that the effects of noise on native and non-native listening are comparable and can be accounted for within the same cognitive architecture for spoken-word recognition.
AB - How does the presence of background noise affect the cognitive processes underlying spoken-word recognition? And how do these effects differ in native and non-native language listeners? We addressed these questions using artificial neural-network modelling. We trained a deep auto-encoder architecture on binary phonological and semantic representations of 121 English and Dutch translation equivalents. We also varied exposure to the two languages to generate ‘native English’ and ‘non-native English’ trained networks. These networks captured key effects in the performance (accuracy rates and the number of erroneous responses per word stimulus) of English and Dutch listeners in an offline English spoken-word identification experiment (Scharenborg et al., 2017), which considered clean and noisy listening conditions and three intensities of speech-shaped noise, applied word-initially or word-finally. Our simulations suggested that the effects of noise on native and non-native listening are comparable and can be accounted for within the same cognitive architecture for spoken-word recognition.
KW - computational modelling
KW - deep neural networks
KW - noise
KW - non-native listening
KW - spoken-word recognition
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M3 - Conference proceeding (ISBN)
SN - 9780991196784
T3 - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
SP - 1902
EP - 1907
BT - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PB - The Cognitive Science Society
Y2 - 25 July 2018 through 28 July 2018
ER -