Replication Data for: Perceiving and identifying vowels in regional accents of English: Evidence from Dutch- and Spanish-speaking L2 listeners

<p><b>Dataset abstract</b></p>

<p>This dataset contains the results of a study on cross-language and second-language vowel perception in Dutch-speaking and Spanish-speaking learners of English. The dataset includes both acoustic similarity predictions and behavioral data from two perceptual tasks. </p>

<p>For the acoustic comparisons, Linear Discriminant Analysis (LDA) models were trained on native vowel data from Dutch and Spanish speakers, recorded in earlier studies. The models were tested on English vowel tokens produced by speakers of Southern British English (S.Eng), Northern British English (N.Eng), and Australian English (AusE), and predict how similar these English vowels are to Dutch and Spanish vowels based on acoustic properties, such as formant frequencies and vowel duration.</p>

<p>In addition to these acoustic predictions, the dataset includes behavioral responses collected during two experimental sessions. In the first session, 40 L1 Dutch and 40 L1 Spanish participants completed (i) a demographic and language background questionnaire, (ii) a cross-language vowel categorization task consisting of 210 trials, and (iii) a general vocabulary test (LexTALE; Lemhöfer & Broersma, 2012). During the cross-language categorization task, participants listened to English vowels produced in the three accents and indicated which vowel from their native language was most similar to that vowel, followed by a goodness-of-fit rating (i.e., how good an example of that vowel the sound was). In the second session, the same participants completed a second-language vowel categorization task with the same 210 trials, in which they were asked to identify which English vowel they heard and to rate how good an example of that vowel it was.</p>

<p> The participants’ cross-language categorization responses were compared to the acoustic similarity scores from the LDA models, to assess how perceived (phonetic) similarity and acoustic similarity align. Participants' identification accuracy in the second-language task was analyzed using a mixed-effects logistic regression model. The repository includes all raw and processed data, the R code used for statistical analysis, and the model outputs.</p>