Department of Psychology, Edge Hill University.
Department of Psychology of Language, Max Planck Institute for Psycholinguistics.
Cogn Sci. 2022 Feb;46(2):e13110. doi: 10.1111/cogs.13110.
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.
口语交流通常发生在嘈杂的环境中，这对语音识别构成了挑战。先前的研究表明，背景噪声的存在增加了与目标词竞争识别的候选词数量，并且这种扩展会影响语音识别的时间进程和准确性。在这项研究中，我们使用计算建模进一步研究了背景噪声存在时竞争过程的时间动态，以及不同语言熟练程度（即母语者和非母语者）的听众如何变化。我们开发了基于自动编码器架构的神经网络模型 ListenIN（在噪声中倾听），该模型学习将语音形式映射到两种语言的含义，并模拟母语者和非母语者的口语理解。我们还检查了模型在在线语音识别过程中的激活状态。这些分析表明，背景噪声的存在会增加竞争词的数量，这些词会参与语音竞争，而且这种竞争在内部和跨语言以及母语者和非母语者的听力中以相似的方式发生。总之，我们的结果支持了一种“许多额外竞争者”的假设，即噪声对语音识别的影响。
