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. Simulation A established that ListenIN captures the effects of noise on accuracy rates and the number of unique misperception errors of native and non-native listeners in an offline spoken-word identification task (Scharenborg et al., 2018). Simulation B showed that ListenIN captures the effects of noise in online task settings and accounts for looking preferences of native (Hintz & Scharenborg, 2016) and non-native (new data collected for this study) listeners in a visual-world paradigm. 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.