Cao Grace Wenling
Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong.
Lang Speech. 2024 Jun;67(2):301-345. doi: 10.1177/00238309231182967. Epub 2023 Aug 2.
Many studies of speech accommodation focus on native speakers with different dialects, whereas only a limited number of studies work on L2 speakers' accommodation and discuss theories for second language (L2) accommodation. This paper aimed to fill the theoretical gap by integrating the revised speech learning model (SLM) with the exemplar-based models for L2 speech accommodation. A total of 19 Cantonese-English bilingual speakers completed map tasks with English speakers of Received Pronunciation and General American English in two separate experiments. Their pronunciations of THOUGHT and PATH vowels, and fricatives [z] and [θ] were examined before, during, and after the map tasks. The role of phonetic dissimilarity in L2 accommodation and L2 category formation in the revised SLM (SLM-r) were tested. First, the results suggested that global phonetic dissimilarity cannot predict Hong Kong English (HKE) speakers' accommodation patterns. Instead, the segment-specific phonetic dissimilarity between participants and interlocutors was found to be positively correlated with the participants' degree of accommodation. In addition, HKE speakers who did not form a new L2 category of [z] were found to significantly accommodate toward their interlocutor, suggesting that L2 accommodation might not be constrained by phonological category. An integrated exemplar model for L2 accommodation is proposed to explain these findings.
许多关于言语顺应的研究聚焦于操不同方言的母语者,而仅有数量有限的研究关注第二语言(L2)学习者的顺应现象并探讨第二语言顺应理论。本文旨在通过将修订后的言语学习模型(SLM)与基于范例的第二语言言语顺应模型相结合来填补这一理论空白。在两项独立实验中,共有19名粤英双语者与操标准发音英语和通用美式英语的英语使用者完成了地图任务。在地图任务前、任务期间和任务后,对他们的THOUGHT和PATH元音以及摩擦音[z]和[θ]的发音进行了考察。对修订后的言语学习模型(SLM-r)中语音差异在第二语言顺应和第二语言类别形成中的作用进行了测试。首先,结果表明整体语音差异无法预测香港英语(HKE)使用者的顺应模式。相反,参与者与对话者之间特定音段的语音差异与参与者的顺应程度呈正相关。此外,未形成新的[z]第二语言类别的香港英语使用者被发现会显著向对话者顺应,这表明第二语言顺应可能不受音系类别的限制。本文提出了一个用于第二语言顺应的综合范例模型来解释这些发现。