Variability in cross-language and cross-dialect perception. How Irish and Chinese migrants process Australian English vowels

2.1 Perception in cross-language contact

Generally, listening is considered language-specific, with phoneme categorisation guided by listeners’ native language(s) (Cutler 2012; see also e.g., Best 1995 on the Perceptual Assimilation Model; Best and Strange 1992; Escudero 2007; Ingram and Park 1997). Phoneme categorisation is also guided by phonetic experience with the contrast (e.g., Ingram and Park 1997; Polka 1995), and the distinctiveness (or saliency) of phoneme cues (Lively et al. 1993). Furthermore, ability and accuracy in distinguishing contrasts can largely be predicted by the phonological inventories of a listener’s L1 (e.g., Flege et al. 1997). For example, as reported in Iverson and Evans (2009), Spanish L1 speakers have a native inventory of five vowels, as compared to German L1 speakers with an inventory of 18 vowels. When given five sessions of high-variability auditory training for English vowels, the results showed that the Germans improved more than the Spanish speakers, despite their more crowded vowel space. However, after an additional ten sessions of training, the Spanish listeners improved as much as the German group, and both groups retained their learning. The findings show that “a larger vowel category inventory may facilitate new learning and support a hypothesis that auditory training improves identification by making the application of existing categories to L2 phonemes more automatic and efficient” (Iverson and Evans 2009: 866).

Conversational experience in the target language has also been found to aid L2 listeners in more native-like category boundary identification, and in discrimination of speech sounds (e.g., Best and Strange 1992 for approximant consonants). Exposure to multiple tokens from a variety of speakers has also been found to assist in non-native/L2 category learning (Clopper 2014; Lively et al. 1993). Studies show that adult listeners may be able to ‘sharpen’ their perception of category boundaries in an L2 and use strategies similar to native speakers, but not gain native-like perceptual abilities (Flege et al. 1997; Weber and Cutler 2006). Vocabulary size in an L2 has also been found to be a strong predictor in discrimination performance in unfamiliar phonotactic contexts in the L2 (Wang et al. 2023).

In a forced-choice vowel identification task, Ingram and Park (1997) looked at vowel production and perception of AusE by L1 Korean and Japanese listeners. Both groups of listeners showed differences in AusE vowel category perception that were linked to their L1 phonology. As an example, compared with Korean listeners, Japanese listeners were able to use duration as a cue to help accurately distinguish long and short AusE vowels, likely because Korean does not have a vowel length contrast. It was argued that Korean listeners had thus not gained experience in using length as a cue to phonetic contrast, unlike Japanese listeners who had learnt this from contrasting the long and short vowels in their L1. The study illustrated how specific ‘attunement mechanisms’ (gained from L1 experience) were employed by the groups. The authors state that, in doing this, “L2 learners confronted with identification of foreign vowels seek to apply or modify perceptual strategies for phonological feature identification that were acquired in the course of first language learning” (Ingram and Park 1997: 365).

Within-group differences also emerged in Ingram and Park (1997) with respect to both age and experience. They discovered that older Korean listeners were better able to categorise the AusE /e-æ/ contrast than were younger Korean listeners. The authors attribute this to the experience that the older listeners have in distinguishing between /e-ɛ/ in Korean, whereas younger listeners were unable to do this, due to the recent merger in /e-ɛ/ in Korean. This study shows that listeners’ categorisation of vowels in an L2 is largely determined by their L1. However, it also shows that listeners who have the same L1, but different experiences of that L1, have different abilities in their L2. This highlights the importance of listener experience with phonetic detail relevant for perceiving cues to phoneme contrasts in an L2 (as shown by Cutler 2012).

2.2 Perception in cross-dialect contact

While it is well-established that L2 listening is language-specific and experience-based, as per the discussion above, perception in cross-dialect contact constitutes a relatively under-researched area. Studies on English show that regional variation means that some listeners of the same language are exposed to different contrasts than others, and thus react differently, in perception (e.g., Loakes et al. 2023, for AusE). There is an ongoing question within the literature concerning the role of experience in speakers recognizing the speech of another dialect by “mapping it onto similar stored exemplars” (Evans and Iverson 2004: 352; see also Pierrehumbert 2002). The mapping of exemplars was investigated by Evans and Iverson (2004) for northern and southern British English vowel perception and Sumner and Samuel (2009) for General American compared with NYC speakers. Clopper (2014) also gives various examples from American dialects in her discussion of the effects of exposure on cross-dialect speech processing. In Shaw et al. (2018), AusE listeners had perceptual tolerance for vowel variation in dialects from London, Yorkshire, Newcastle (UK) and New Zealand, in all but a few contexts. In other words, the AusE listeners were able to perceptually assimilate phonetic details into abstract vowel categories, despite the differences in accent they were hearing. However, being exposed to a short passage read by a native speaker of those dialects before completing the same task did not result in any significant differences, meaning there was little sign of rapid adaptation (Shaw et al. 2018: 1).

Nycz (2013) studied speakers of Canadian English who had moved to New York City and their perception and production of (oh)/(o) pairs (represented as such in the paper), which are typically merged in Canadian English and split in New York City English. She found that the speakers neither produced nor perceived any differences in the pairs in a minimal pair task, but in wordlists and conversational data, they showed signs of a low back vowel contrast. In a further study, Nycz (2019) found that the same speakers of Canadian English were more likely to have this non-native low back vowel contrast if they were married to a New Yorker. This variable was more significant than that of LOR in New York City, or identification as New Yorker. While not a perception study, Nycz (2019) shows the importance of examining social networks as sources of input in studies of D2/L2 acquisition. The present study builds on this by incorporating a social network metric (see Section 4.2.2).

A study investigating effects of experience on intelligibility, but in a second language context (Pinet et al. 2015), found that Mandarin listeners with intermediate English proficiency had similar levels of speech-in-noise recognition of standard British accents as native British English speakers, leading the authors to conclude (perhaps rather controversially) that, rather than experience, it could be that “standard accents […] have acoustic-phonetic features that make them inherently more intelligible” (Pinet et al. 2015). These findings suggest that the role of experience and exposure in L2/D2 perception can also be inconsistent, and this is an aspect we explore in the present study.

2.3 The front lax vowels /ɪ e æ/ in perception and production

4.1 Participants

The present study stems from a larger project on second language and second dialect acquisition by recently-arrived migrants in Melbourne (see Diskin et al. 2018, 2019a, 2019b, 2019c). All data collection took place in 2017 on The University of Melbourne campus. The participants (see Table 1) completed a variety of tasks, including a studio-recorded wordlist and sociolinguistic interview, a recorded wordlist with simultaneous ultrasound tongue imaging, and a comprehensive socio-demographic background questionnaire (based on Horvath 1985; Llamas 1999; Lonergan 2013).^[1] The final task was a forced-choice vowel categorisation task presented on a custom-made app for the iPad (see Loakes et al. 2014a, 2023), which is the task we report on in this paper, and completed by the three listener groups. The totality of the tasks took approximately 2 h. The vowel categorisation task took approximately 20 min to complete and was administered in a sound-proof perception laboratory under the supervision of a researcher and/or a research assistant.

All but two of the Australian participants were born and raised in Melbourne and had never spent longer than one year living outside of the greater Melbourne area.^[2] This residence requirement for the Australians was one of the criteria for participation in the study. Criteria for the Irish and Chinese participants were that they should have moved to Australia as adults (aged 18+, although one Chinese participant was 17 when he moved). The Chinese participants came from a variety of regions across Mainland China and Hong Kong. The Irish participants came from towns and villages across the island of Ireland, encompassing both the Republic of Ireland and Northern Ireland (which include a diversity of dialect regions – see Corrigan 2010; Kallen 2013), so there is some dialectal difference amongst these participants (Diskin et al. 2019b – see also Section 2.3.2 above). The years of birth of the participants ranged from 1976 to 1995 (Table 1), so they are relatively homogenous in terms of age and mostly younger than the participants in Loakes et al. (2023) or Schmidt et al. (2021).

4.2 Method and materials

4.3 Analysis

We first present descriptive comparisons between language/variety groups using proportions (or observed probabilities) of responses across the continua, with the mean per continuum step plotted, along with 95 % (bootstrapped) confidence intervals. These comparisons are then examined using mixed effects binomial logistic regression models, which were fit using the glmer function in lme4 (Bates et al. 2015) using the RStudio IDE (RStudio Team 2020) for R (R Core Team 2020). We began with an intercept-only model and proceeded with testing explanatory fixed factors (continuum step, gender, language/variety), as well as interactions between these factors, in a stepwise step up fashion following procedures in Schweinberger (2021). The final model in each case is the minimal adequate model to account for the variability in the data using these factors. Results of likelihood ratio tests comparing these final models with the baseline intercept-only model are reported. In all cases, listener was used as the random effect. Treatment coding was used for all categorical predictors and polynomial coding was used for the ordinal predictor step. We chose to examine continuum step as a factor rather than phonetic context, as it was more fine-grained than looking at phonetic context alone (six levels rather than three – see Table 2). Adding both phonetic context and continuum step as factors would have reduced the stability and power of the model.

Model evaluation summaries are presented in Table 4, including results of likelihood ratio tests (“LRT”: comparison of final model with baseline), and model fit parameters, the C-value (which indicate a good model fit above 0.8 [Baayen 2008: 204]) and Somer’s D_xy, which ranges from 0-1 with higher values, indicating greater predictive power.

In order to assess the effect of LOR, social network, gender, and proficiency (as relevant), we chose a non-parametric modelling method, as adding these factors to the logistic regressions either violated the assumptions of the models or resulted in convergence failures. We acknowledge that, at 36 participants, the sample size is small as compared to comparable studies in speech perception, and that we are incorporating many variables. In order to offset issues of reliability, we adopt the non-parametric method of conditional inference trees (Barth and Schnell 2022; Levshina 2020; Schweinberger 2021; Tagliamonte and Baayen 2012). Conditional inference trees are a method for regression and classification based on binary recursive partitioning (Levshina 2015: 291) and are particularly suitable for situations of small n and large p, where n is the number of observations and p is the number of predictors (Tagliamonte and Baayen 2012). A non-parametric method such as conditional inference trees has fewer assumptions around sample size compared to e.g., a generalised linear mixed effects model. It helps to describe the data, rather than make inferences based on the data.

step always emerged as the predominant predictor in our conditional inference trees. It was responsible for the first binary split, being highly significant, and often responsible for multiple splits. However, the analysis including other social factors allows us to understand some of the individual-specific factors that condition listener responses in the Irish and Chinese cohorts. Results of this modelling are discussed in Sections 5.4 and 5.5. Note that for Irish listeners, for the /hill-hell/, /mill-Mel/, and /Mel-Mal/ continua, only step significantly predicted responses using this method. For Chinese listeners, for the /hell-hal/ and /Mel-Mal/ continua, step was the only significant factor.

5.1 /hVt/ condition

The results in Figure 1 for the /hVt/ condition show that in the case of /hit-het/ for the native AusE listeners the crossover point is at the acoustic midpoint (Step 4). This response was predicted. However, there were differences between the listener groups for this stimulus. At Step 1, complete agreement was only attained by the Irish listeners, although the AusE listeners attained almost 100 % agreement by Step 6. The Irish and AusE listeners had significantly different crossover points for /i-e/, with the Irish listeners crossing over to hearing /het/ at Step 6 – a full two steps later than for the AusE listeners. The Chinese listeners did not have a crossover point for this continuum and had very low levels of agreement with only 42 % of responses being het at Step 7. Therefore, in 58 % of cases, at Step 7, some Chinese listeners were still hearing hit. The Irish also had low levels of agreement at Step 7, with just 59 % of responses being het. Overall, these differences are confirmed by our statistical modelling: the final minimal adequate model performed significantly better than an intercept-only baseline model. The model returns a significant interaction between step and language/variety, emphasising the differences between the AusE and Chinese responses across the /hit-het/ continuum (z = 3.32, p = 0.001).

Figure 1:

Observed probabilities/proportions by continuum step /hit-het/ (left panel) and /het-hat/ (right panel) in blue (Australian), red (Chinese) and green (Irish).

AusE responses to /het-hat/ were less consistent with predictions for this condition than for /hit-het/: they had an early crossover point at Step 2 and a bias for hat. This bias was also visible for the Irish, whose crossover points were before the acoustic midpoint. However, the Chinese had a crossover at approximately the acoustic midpoint (Step 4). There was significant difference in crossovers from /e/ to /æ/ between the AusE and the Chinese listeners. There was also a lack of certainty for this stimulus, with approximately 80 % agreement at Step 1 for all three groups, where 100 % would be predicted. By Step 6, the Irish listeners, and most of the AusE listeners, had reached 100 % agreement and switched to hearing hat. However, the Chinese listeners were not all in agreement. Logistic regression modelling returned a significant two-way interaction between step and language/variety, confirming the Chinese listeners’ performance as most distinct from the other two groups of listeners, exhibited by their preference for het across the continuum (z = 3.07, p = 0.002). The results for the /hVt/ condition are summarised in Table 5.

5.2 /hVl/ and /mVl/ condition: coarticulatory effects on categorisation

Figure 2 shows the responses for /hVl/ and /mVl/ condition. These stimuli test the effects of coarticulation on processing: the effect of a pre-lateral context as well as a nasal onset. In the case of /hill-hell/, there were significant differences between the responses across all three groups. The three groups have high rates of agreement for hill responses at Step 1, but for the Irish and, to a lesser extent, the AusE and Chinese listeners, the hill response is consistently sustained until after Step 6, followed by a steep decline in hill when the category crossover occurs. However, there is still only 62 % agreement at Step 7 for the Irish listeners. For the AusE listeners, there is a more gradual decline in hill responses until the category crossover at approximately Step 5. While the majority of AusE listeners had switched to hearing hell by Step 7, large proportions of the Irish (38 %) and Chinese (31 %) responses remained hill. For this option, the Irish and the Chinese exhibited similar categorisation behaviour. In our modelling, where the Chinese and Irish were compared with the AusE listeners, significant main effects of language/variety were found for the Chinese (z = −2.80, p = 0.005) and the Irish (z = 2.14, p = 0.032), who were both overall more likely to respond with hill as compared to the AusE listeners. The significant interaction between step and language/variety shows Chinese listeners had significantly more hill responses towards the end of the continuum, i.e., Steps 6 and 7 (z = 4.15, p < 0.001).

Figure 2:

Observed probabilities/proportions by continuum step for /hill-hell/ (left panel) and /mill-Mel/ (right panel) in blue (Australian), red (Chinese) and green (Irish).

For /mill-Mel/, the responses across the three listener groups were very similar to /hill-hell/, with the Irish sustaining mill responses for longer than the AusE listeners. The AusE listeners switched from hearing mill to Mel at Step 5, after the acoustic midpoint, whereas the Irish crossed over at Step 6 and the Chinese at Step 7. As with /hill-hell/, there were significant differences between the responses across all three groups. However, the crossovers for the AusE and Irish groups were earlier in this pre-lateral, nasal onset condition (at Steps 5 and 6 respectively) as compared to the pre-lateral condition without the nasal onset (at Steps 6 and 6–7 respectively). In this nasal onset condition, the AusE and Irish listeners had high levels of agreement by Step 7 (between 89 and 100 %), but the Chinese listeners had agreement of only 54 % by Step 7. Regression modelling shows similar patterns to those for the /hill-hell/ continuum, with significant main effects of language/variety, whereby both the Chinese (z = −2.56, p = 0.011) and the Irish listeners (z = 2.37, p < 0.001) had significantly more mill responses than the AusE listeners. There was also an interaction between step and language/variety, showing that the Chinese listeners had more mill responses than the Irish or the AusE listeners towards the end of the continuum (z = 4.72, p < 0.001).

5.3 /el-æl/ condition: predicted merger context

Figure 3 shows that for /hell-hal/, the crossover for the AusE listeners is at the acoustic midpoint, whereas the Irish and Chinese listeners have similarly late crossovers at Steps 6 and 7 respectively. This mirrors the categorisation behaviour of the Irish and Chinese listeners for other continua, with a pattern of late crossovers (and less overall agreement, particularly towards the end of the continuum.). For this continuum, the AusE responses were significantly different from the Irish and Chinese responses, but the Irish and Chinese responses were not significantly different from each other. Modelling showed significant main effect of language/variety, whereby Irish listeners have more hell responses overall (z = 3.28, p = 0.001), and an interaction between step and language/variety, whereby Chinese listeners retained more hell responses towards the end of the continuum (z = 2.60, p = 0.009).

Figure 3:

Observed probabilities/proportions by continuum step for /hell-hal/ (left panel) and /Mel-Mal/ (right panel) in blue (Australian), red (Chinese) and green (Irish).

For the /Mel-Mal/ stimulus, all three sets of responses were significantly different from one another. In this nasal onset condition, the crossovers are somewhat earlier for the AusE listeners as compared to the /hell-hal/ stimulus:, there is an abrupt downwards trend from Mel towards Mal around Step 3. For the Irish and Chinese listeners, the trend is not as abrupt. The crossover for the Chinese listeners is later in the nasal onset condition, at Step 7. Furthermore, while the AusE and Irish listeners have fairly high levels of agreement, the level of agreement among the Chinese is only 65 % at Step 7, mirroring their levels of agreement in the other nasal onset condition: /mill-Mel/.

Both /el-æl/ contexts were predicted to be ‘merger’ conditions, with AusE listeners answering at random and/or having crossovers that were not at the acoustic midpoint. However, both with and without a nasal onset, there is no strong evidence of a merger in perception by any of the three listener groups, although low agreement rates at endpoints of some of the continua (e.g., Step 1 for AusE listeners for /Mel-Mal/) suggest potential merger among some listeners.

The results of modelling show a significant main effect of language/variety, whereby the Chinese had significantly more Mel responses than the AusE and Irish listeners (z = 4.16, p < 0.001). There is also an interaction between step and language/variety, showing that Chinese listeners have more Mel responses towards the end of the continuum (z = 3.94, p < 0.001), whereas Irish listeners have significantly more Mal responses towards the end of the continuum (z = −3.32, p = 0.001). A summary of results for the /hVl/ and /mVl/ conditions is provided in Table 6.

5.4 Second dialect perception: Irish listeners

As discussed in the previous sections, Irish listeners were found to have different categorisation behaviour depending on the stimulus (Figure 4). Some of the responses to continua such as /het-hat/ indicated early crossovers, but high levels of agreement. Other responses, such as those for /hit-het/, indicated late crossovers and low levels of agreement. With the aim of investigating the effect of experience and exposure on perception, in sub-Sections 5.4.1 and 5.4.2, we analyse effects of LOR and social network among the Irish listeners, using conditional inference trees (see Section 4.3). In all cases, only the conditional inference trees for continua that yielded significant differences are reported.

Figure 4:

Group-level responses for Irish listeners to the six stimuli in the present study: /hit-het/ (red); /hill-hell/ (mustard); /mill-Mel/ (green); /het-hat/ (aqua); /hell-hal/ (blue); /Mel-Mal/ (pink).

5.4.1 Effect of LOR among Irish listeners

The Irish listeners had been living in Australia for an average of 5 years 8 months (see Table 1). To test for the effect of LOR, we use conditional inference trees based on binary recursive partitioning.^[4] They are to be read as follows: the ‘tree’ shows “all possible splits that are significant at the level of p < 0.05. The ovals contain the names of the variables selected for the best split, as well as the corresponding p-values. The levels of the variables are specified on the ‘branches’” (Levshina 2015: 294) so that in the case of Figure 5, the levels on the branches on the far left are the steps from the task, i.e., ≤Step 4, or >Step 4. The bar plots at the bottom (‘leaves’) show the proportion of, in the case of Figure 5, hell being selected over Hal in each end node (‘bin’), which “contains all observations with a given combination of features. The number of observations in each bin is shown in parentheses above the boxes” (Levshina 2015: 294).

Figure 5:

Conditional inference tree for /hell-Hal/ continuum by Irish listeners, showing significant effect of LOR.

Figure 5 shows that step, which is the first split in the tree, plays the most important role in conditioning responses to the /hell-Hal/ continuum by the Irish listeners. This accords with the results discussed in previous sections. However, the analysis shows that LOR also plays some role in conditioning responses. Specifically, after step 4, those with LOR of ≤3.5 years had more Hal responses than hell (Node 4), which resembles the behaviour of the AusE listeners, who crossed over at Step 4 (the acoustic midpoint). Those with LOR of >3.5 years had more hell responses at steps ≤5. Above Step 5 in the longer LOR group, however, there is a roughly equal split between hell and Hal responses (Node 7), which differs from the AusE listeners, who by Step 5 had approximately 75 % Hal responses. The tree shows that longer LOR does not predict more Australian-like categorisation behaviour (more Hal responses by Step 4), in fact, quite the opposite. Those with shorter LORs are the most likely to have Hal responses around Step 4 (resembling the AusE crossovers – see Node 4), which goes against predictions that increased LOR would result in more Australian-like crossovers.

LOR also had an effect on responses to the /hit-het/ continuum among Irish listeners (Figure 6). In this case, the effect was evident above Step 4 (to the right of the tree), and among those with LORs below and above 6 years. Within this, the effect is mediated by gender, such that for those with longer LORs (>6 years; to the right of leaf 5), men had more hit responses (Node 13), while women had more het responses (Node 12), representing a more Australian-like crossover, as the AusE listeners had crossed over at the acoustic midpoint (Step 4). For those with LORs at six years and under (to the left of leaf 5), the men above step 5 show the opposite pattern to the men with longer LORs – they are more likely to respond with het. However, women in Node 9 show similar Australian crossovers, with a preference for het after Step 5. This suggests an interaction between female gender and increased LOR in predicting more Australian-like categorisation behaviour.

Figure 6:

Conditional inference tree for /hit-het/ continuum by Irish listeners, showing significant effect of LOR and gender.

5.4.2 Effect of Irish social network among Irish listeners

Figure 7 shows that Irish social network score (ranging from 0.0, i.e., no-one out of the network is Irish, to 0.6, i.e., four out of the six in the network is Irish) plays a role in conditioning responses to /het-hat/ for the Irish listeners. Specifically, between steps 2 and 4, women who had an Irish SNS of ≤0.16 (leaf 7) had more het responses (Node 8) than hat. This does not mirror AusE responses, which crossed from het to hat early, just after Step 2. Conversely, the women with an Irish SNS of >0.16 had 80 % hat responses below Step 4, which resembles the AusE crossovers, but counters predictions that a lower Irish SNS would result in more Australian-like behaviour. Node 10, showing an effect of male gender below Step 4, also shows a more Australian-like behaviour. In this sense, no one gender can be said to be clearly predicting Australian-like crossovers, but it is also of note that the Irish in general were fairly close to the AusE group for categorisation of this stimulus (see Table 5). A summary of results of extralinguistic factors for the Irish listeners is provided in Table 7.

Figure 7:

Conditional inference tree for /het-hat/ continuum by Irish listeners, showing significant effect of Irish Social Network Score (SNS_IE) and gender.

Table 7:

Summary of significant effects of extralinguistic factors for the Irish listeners.

Stimulus	Irish
/hell-hal/	Shorter LOR predicts more Hal responses LOR does not correlate with Australian-like behaviour
/hit-het/	Women with LOR of 6+ years: more het responses Men with LOR of <6 years: more het responses after Step 5
/het-hat/	Women with Irish SNS ≤0.16: more het responses than hat Lower Irish SNS does not correlate with Australian-like behaviour

5.5 Second language perception: findings from Chinese speaker-listeners

As discussed in previous sections, the Chinese listeners had low levels of agreement, at 40–50 % in many cases, indicating uncertainty and that listeners were answering at random for most of the stimuli with the exception of /het-hat/ (Figure 8). They also had more within-group inconsistencies than the Irish or AusE listeners and had later crossovers for all stimuli, even by Step 7, which is the point at which the contrast should be the most obvious. Furthermore, the Chinese listeners appear to be generally hearing closer vowels in all cases except /het-hat/. This is not a task effect, since the trials were randomized, indicating that in many cases the listeners are selecting the phonetically closer option out of the two in a contrast. Overall, the findings indicate that, despite exposure through living and working in Australia, these Chinese participants still experience differences in making phonemic judgements about AusE stimuli as compared to L1 listeners. In the sub-sections that follow, we will analyse potential extralinguistic effects of proficiency in English and social network on the categorisation behaviour of the Chinese listeners.

Figure 8:

Group-level responses for Chinese listeners to the six stimuli in the present study: /hit-het/ (red); /hill-hell/ (mustard); /mill-Mel/ (green); /het-hat/ (aqua); /hell-hal/ (blue); /Mel-Mal/ (pink).

5.5.1 Effect of proficiency in English

Figure 9 shows significant effects of English proficiency, as measured by IELTS score, but not in the predicted direction. Of note is that it shows that above step 5 (to the right of the tree), those with lower IELTS (≤6), which consisted of two participants (CH_018_F and CH_026_F), have crossed over almost categorically to answering with hell (Node 12), which resembles the AusE behaviour for this stimulus. Meanwhile, those with higher IELTS scores (>6) show more variation and less agreement, with almost 40 % hill responses even after Step 6. These results do not indicate that a higher IELTS score predicts more Australian-like categorisation behaviour, where crossover was at approximately step 5. Below step five (to the left of the tree), there are also differences based on IELTS score, and these are moderated by social networks, which are discussed further in Section 5.5.2.

Figure 9:

Conditional inference tree for /hill-hell/ continuum by Chinese listeners, showing significant effect of English proficiency (IELTS).

Figure 10 shows significant effects of English proficiency, again moderated to some extent by social networks (leaf 4). There is a similar pattern to what was found for the /hill-hell/ continuum, whereby those with IELTS of ≤6 had more Australian-like crossovers. That is, above Step 5, for those with IELTS above 6.5, there are significantly more mill responses (Node 13), while for those with scores ≤6.5, there are significantly more Mel responses (Node 12), which mirrors the Australian crossovers which occurred just after the acoustic midpoint at Step 5 for this continuum. The left of the tree confirms this trend, in that it shows us that IELTS scores ≤6 have more Mel responses after Step 4 (leaf 3; Node 7).

Figure 10:

Conditional inference tree for /mill-Mel/ continuum by Chinese listeners, showing significant effect of English proficiency (IELTS).

5.5.2 Effect of Chinese social network

As seen above with the Irish group, for the Chinese group, there was a significant effect of having more Chinese in one’s social network, as measured by the Chinese Social Network Score (SNS_CH; Figure 11). Specifically, at and below step four of the continuum, listeners with Chinese SNS of 0.16 or lower (leaf 3) had about 60 % hit versus het responses (Node 4), which is more in the direction of an Australian-like categorisation behaviour as compared to those with higher Chinese SNS (above 0.16) who responded almost categorically with hit (Node 5).

Figure 11:

Conditional inference tree for /hit-het/ continuum by Chinese listeners, showing significant effect of Chinese Social Network Score (SNS_CH).

Examining the effect of social networks for Chinese listeners’ responses to the /het-hat/ continuum, a significant effect of Australian social network was found (Figure 12). This showed that, between steps 1 and 5, those with low SNS_AU scores (≤0.167; leaf 4) had slightly more hat responses than het responses (Node 5), which is more in the direction of the Australian listeners, who had very early crossovers in this condition. For those with higher scores (>0.167), responses were primarily conditioned by continuum step (with hat responses gradually increasing across the continuum – Nodes 7, 8 and 9). This shows that a higher Australian social network score does not correlate with more Australian-like categorisation.

Figure 12:

Conditional inference tree for /het-hat/ continuum by Chinese listeners, showing significant effect of Australian Social Network Score (SNS_AU).

Social network score also affected how Chinese listeners responsed to the /hill-hell/ continuum. This pattern was complex, however, as it was also conditioned by IELTS score. Returning to Figure 9, we see that at or below Step 5, for those with IELTS ≤6.5 (leaf 2), those with lower Chinese SNSs (≤0.667), had about 50 % hell responses (Node 4), while those with high Chinese SNSs (>0.667) responded almost categorically with hill (Node 5). Since Australians had a crossover at about Step 5 in this condition, there is an indication here that at least some of those with fewer Chinese people in their social network were approximating the Australian behaviour. The other split in the data for SNS (leaf 7) is less relevant, as it occurs at or below Step 4, whereas the Australian crossover was closer to Step 5.

A similar effect – whereby IELTS score dominated the effect of social network score – was seen in the analysis of the /mill-Mel/ continuum responses (Figure 13). Here, for those with IELTS scores ≤6, when responding at or below Step 4 (leaf 3), those with lower Chinese SNSs tended to respond equally with mill or Mel (Node 5). Since the Australian crossover was just after Step 5 in this condition, we can see that, again, a lower Chinese SNS score is approximating an Australian-like behaviour – at the very least in comparison to those with higher SNS (>0.667), who respond with mill categorically. A summary of results of extralinguistic factors for the Chinese listeners is provided in Table 8.

Figure 13:

Conditional inference tree for /mill-Mel/ continuum by Chinese listeners, showing significant effects of Chinese Social Network Score (SNS_CH) and IELTS score.

Table 8:

Summary of significant effects of extralinguistic factors for Chinese listeners.

Stimulus	Chinese
/hill-hell/	IELTS ≤6: more Australian-like categorisation (crossover around Step 5) Higher IELTS does not correlate with Australian-like behaviour Some with lower Chinese SNS approximate AusE crossovers
/hit-het/	Lower Chinese SNS: more Australian-like crossovers
/het-hat/	Low Australian SNS results in slightly more Australian-like crossovers
/mill-Mel/	IELTS scores ≤6–6.5 have more Australian-like crossovers Higher IELTS does not correlate with Australian-like behaviour Lower Chinese SNS score: approximate Australian-like behaviour

6.1 Study 1: categorisation of /hVt/, /hVl/ and /mVl/ for the AusE listeners

Our first study aimed to investigate how native Australian (D1) listeners categorised the AusE vowel stimuli /ɪ e æ/, and whether these categorisation decisions would be affected by coarticulation. First, we had predicted that they would categorise /hVt/ at the acoustic midpoint (Step 4). This prediction was confirmed for /hit-het/. However, categorisation for D1 listeners was not at the acoustic midpoint for /het-hat/, with an earlier crossover point than for /hit-het/, at Step 2. Indeed, the /het-hat/ contrast posed some difficulty for D1 listeners, with only approximately 80 % agreement even at Step 1, and an overall bias towards hat. The responses suggest that D1 listeners may have required a phonetically lower vowel for het. A similar bias for hat was observed for the younger speakers in Loakes et al. (2023), which uses the same experiment on different cohorts of listeners. Lexical frequency may have also played a role in responses, since het is a relatively infrequent word in English, particularly as compared to the frequency of hat, as also observed in Loakes et al. (2023), and so listeners were likely more motivated to choose hat. This supports an exemplar-based theory of speech perception, whereby “a phonological category is defined by a distribution in a perceptual space that depends on its remembered exemplars” (Harrington et al. 2008: 2832; see also Pierrehumbert 2002).

We predicted that the effect of a following lateral would lower the vowel in perception and result in later crossovers than for the /hVt/ condition. This prediction was also confirmed. The D1 listeners had crossovers at Step 5 for /hill-hell/ and at Step 4 for /hell-Hal/, representing a later crossover of one step for /hill-hell/ as compared to /hit-het/, and a later crossover of two steps for /hell-Hal/ as compared to /het-hat/. For the /mVl/ condition, it was predicted that the effect of the nasal onset would raise the vowel in perception, resulting in earlier crossovers. The D1 listeners had crossover points at Step 4.5 for /mill-Mel/ and at Step 3.5 for /Mel-Mal/. Thus, this prediction was also confirmed, as this constituted an earlier crossover point of 0.5 steps both for /mill-Mel/ as compared to /hill-hell/ and for /Mel-Mal/ as compared to /hell-Hal/.

Despite the effects of the nasal onset and following lateral, overall, we found no strong evidence of a celery-salary merger in perception among the D1 listeners, contrary to findings by Loakes et al. (2023) for AusE speakers in southern Victoria (including Melbourne), although we did find low agreement at the /mel/ end of the /mel-Mal/ continuum, suggesting uncertainty. This is despite the fact that our listeners are born and raised in Melbourne, where the merger in production has been found among some (although not all) speaker-listeners (Loakes et al. 2017). Reasons for these differences could include the fact that the listeners in Loakes et al. (2023) are mostly (though not exclusively) older than the listeners in the current study, with some participants born as late as 1996, but many middle-aged and elderly speakers born as early as 1936. Loakes et al. (2023) found clear age differences, with more merger behaviour in perception observed among older listeners. Similar findings among older listeners were found in Ocean Grove, Victoria in Schmidt et al. (2021).

Further work into the production of the merger has shown that variability is fine-grained, with many individual differences (Diskin et al. 2019a, c; Schmidt et al. 2021). It must also be noted that the contrast in the present study is particularly complex for listeners, as it includes both a nasal onset and a following lateral. It was also found to be complex for the L2 listeners in the present study (see Section 5.3). Furthermore, as the /Mel-Mal/ contrast only has proper names as options, this likely poses no lexical competition for the AusE listeners, resulting in both Mel or Mal being plausible, although in Loakes et al. (2023), it was shown that younger listeners preferred Mel and older listeners preferred Mal, which was explained as generational differences in popularity of these names. Nonetheless, it is possible these are both equally unusual names for the Irish and Chinese listeners, so lexical frequency may be uneven amongst the various cohorts.

6.2 Study 2 Part 1: categorisation of /hVt/, /hVl/ and /mVl/ for the L2/D2 listeners

Taking all three contexts (/hVt/, /hVl/ and /mVl/) as benchmarks for comparison between the D1 as compared to the D2 and L2 groups, an ‘Australian-like categorisation behaviour’ could, for these listener groups, be described as consisting of: (1) no perceptual difficulty between the vowels /e æ/ generally (i.e., they are contrastive), (2) a midpoint crossover for /hit-het/ and an early crossover for /het-hat/; (3) a crossover for /hVl/ that is later than the crossover for /mVl/, with no perceptual difficulty for the pre-lateral contrast (i.e., no merger in perception).

While the D1 listeners categorised the /hit-het/ contrast predictably at the acoustic midpoint, D2 and L2 listeners had significantly later crossovers for /hit-het/: at Step 6 for D2 listeners and with no discernible crossover point for L2 listeners (no agreement even by Step 7). This shows that, compared to the D1 listeners, the Irish listeners had different, but internally consistent, crossovers, whereas the Chinese listeners had overall difficulty with this contrast. This supports findings by Chen et al. (2001) that English vowels not found in the Mandarin inventory (in this case, /ɪ/) could present perceptual difficulties for Mandarin listeners, as well as the findings of Iverson and Evans (2009), who found that listeners with a larger vowel inventory in their L1 may be at an advantage in learning an L2 vowel system than those with a small inventory, such as e.g., Mandarin. It does not support the findings of Jia et al. (2006) that simply being exposed to the L1 results in increased mastery of discrimination of phonology, as the Chinese participants were all immersed in an AusE setting, and there was no correlation between LOR and more Australian-like categorisations.

All listener groups behaved similarly in their categorisation of /het-hat/, with uniformly early crossovers (between Steps 2 and 3.5) and no significant differences between the responses. This suggests that the D2 and L2 groups mirrored the AusE categorisation behaviour for the /het-hat/ contrast. It is known that AusE vowels have lowered and retracted over time in production (Cox and Palethorpe 2008) and perception (Mannell 2004; also shown by Loakes et al. 2023) and this is also corroborated by our findings for /het-hat/. Since this is likely a contrast that L2/D2 listeners are exposed to in the community, this lends some weight to the proposal that experience with different contrasts can allow L2/D2 listeners to map onto similarly stored exemplars (Evans and Iverson 2004: 352). However, what we observe for the D2 listeners is that they mirror in perception what has been found for their production, with even lower and more retracted vowels as compared to the D1 cohort (Diskin et al. 2019b). Furthermore, since we found that for the /het-hat/ stimulus, Chinese with a lower Australian SNS had a more Australian-like crossover, it is possible that this contrast was simply more straightforward for the L2/D2 listeners due to mapping from their L1/D1. This is supported by their high rates of agreement for this stimulus, despite the fact that Mandarin does not have the mid front monophthongs /e/ or /eː/ (Duanmu 2007), although it does have a low vowel /a/, of which [æ] is an allophone (Wang et al. 2022: 161). This suggests that the L2 listeners were able to map somewhat unfamiliar sounds onto (either new or existing) phonological categories. The overall preference for hat may also have been due to the effect of hat being a more frequent word than het, as discussed in Section 6.1. This may particularly be the case for the L2 group, who may have had no previous exposure to the infrequent word het.

The effect of the lateral coda resulted in a downshifting in perception for all listener groups, to differing degrees. Both the D2 and L2 groups had later crossover points for /hill-hell/ and /hell-Hal/ than the D1 group, in this way behaving similarly to each other. However, for /hill-hell/, while the majority of the D1 listeners had switched to hearing hell by Step 7, still many of the D2 and L2 listeners had a preference for hill (i.e., there was low agreement at Step 7). This shows similar categorisation behaviour among both the D2 and L2 listeners for this continuum, mirroring their behaviour for the /hit-het/ contrast. This result suggests the vowel contrast in AusE may be particularly complex, regardless of the presence or absence of a following lateral. However, we do find support for the role of experience and exposure for this stimulus, as some listeners with a lower Chinese SNS (i.e., fewer Chinese in their social network) had more Australian-like crossovers. For /hell-Hal/, most, but not all, D2 and L2 listeners had switched to hearing Hal by Step 6, whereas this crossover occurred at the acoustic midpoint for the D1 listeners. This again mirrors the patterns for /hit-het/. No significant extralinguistic factors emerged for the /hell-Hal/ stimulus, other than the fact that Irish listeners with a shorter LOR had more Australian-like categorisations, meaning that the role of experience/exposure to the D2 is likely minimal for this contrast.

The crossovers between /e æ/ in the nasal onset condition were not perceptually difficult for the D1 or the D2 listeners. In the case of /mill-Mel/, the D2 listeners sustained mill responses longer than the D1 listeners, but also had high levels of agreement at the beginning and end of the continuum. Since no extralinguistic factors emerged as significant for this stimulus, it is more likely that the Irish listeners were mapping onto stored exemplars from their L1, rather than exhibiting any changes in categorisation behaviour over time. For the L2 listeners, however, the /mVl/ context appeared to present difficulty, with listeners responding below chance level at the /Mal/ end of the continuum.

Our prediction that the D2 and L2 listeners would display different categorisation behaviour to the D1 listeners, due to a ‘perceptual filter’ from their native dialect/language (Best 1995; Best and Strange 1992; Escudero 2007), was supported somewhat, particularly for the D2 listeners in the /hit-het/ and /hVl/ and /mVl/ contexts. However, the D2 listeners also aligned somewhat with the D1 listeners when it came to their categorisation behaviour for the /het-hat/ contrast and their upshifting and downshifting behaviour in the /hVl/ and /mVl/ contexts respectively. This shows that D1 and D2 listeners patterned more closely together in their categorisation behaviour, which is understandable since both groups share the same native language. The L2 listeners generally behaved differently to the D1 and D2 listeners, except for the /het-hat/ contrast, where there were no significant inter-group differences. Since the L2 listeners showed evidence of perceptual difficulty and lack of agreement in all contexts but one, it can be deduced that they were still very much guided by their L1 in distinguishing between contrasts in the L2. The difficulty appeared to be more acute in the /mVl/ conditions (with very late crossovers) than in the /hVl/ conditions, as well as for /hit-het/. This could be explained by the fact that Mandarin speakers have been found to have less carryover of nasality into the following vowel than speakers of English (Li et al. 2020).

6.3 Study 2 Part 2: the effect of LOR, social networks, proficiency and gender

We predicted that, due to listening experience gained while living and working or studying in Australia, D2 and L2 listeners would have more Australian-like vowel categorisations (as established via the D1 benchmark group) if they had lived in Australia for longer; had more Australians in their social network; and in the case of the L2 listeners, a higher proficiency in English. We also predicted that listeners would have less Australian-like categorisations if they had a high proportion of people of their own nationality (Irish or Chinese) in their social network. We predicted no gender effects in line with Loakes et al. (2023).

Our prediction regarding LOR was not supported. For /hell-hal/, after step 4, D2 listeners with LOR of less than 3.5 years had more Hal responses than hell responses (more Australian-like), while those with LORs of longer than 3.5 years had more hell responses at step 4 or 5 (less Australian-like). This suggests little effect of LOR on the potential for an Australian-like categorisation behaviour among the D2 listeners. The finding supports similar (but not identical) work by e.g., Shaw et al. (2018), showing that vowel assimilations can be unaffected by exposure to other talkers’ accent-specific variation (in the Irish case, exposure is via living and working in Australia). It also supports work by Nycz (2019), showing that LOR in a D2 community has less of an effect on acquisition of a contrast than the effect of being married to a speaker of the D2 – in other words, exposure needs to be both frequent and sustained for D2-like contrasts to be acquired. Our findings and those of Nycz (2019) provide impetus for further studies to incorporate social network analyses and work focusing on the quality as well as quantity of input.

For the /hit-het/ stimulus, both LOR and gender had an effect on the D2 listeners’ categorisations, where above step 4 and among those with longer LORs, women had more het responses. The D1 listeners crossed over to het responses at step 4 in this condition, suggesting that, among the female listeners at least, LOR may have had some effect on attaining an Australian-like categorisation. However, an effect was also found for men with an LOR of 6 years or less having more het responses after Step 5. This suggests that neither LOR nor gender were positively correlated with Australian-like categorisations. Regarding the differences in gender, previous studies on AusE using these stimuli have shown no gender effects for listeners (e.g., Loakes et al. 2014a, 2023), leading us to predict that this result is likely based on differences in social networks and increased resultant exposure to AusE voices in our study. The overall weak effect of LOR suggests that simply residing in another country is less important than the type and quality of exposure that a D2 listener receives during this time, which supports work by Ingram and Park (1997) and Polka (1995) on the role of listener experience in perception.

Similarly, for the L2 listeners with the /hit-het/ stimulus, at and below step four of the continuum, listeners with a low degree of Chinese people in their social network provided more hit than het responses (somewhat more Australian-like), but those with more Chinese people in their social network responded almost categorically with hit at or below step 4 of the continuum (not at all Australian-like). This suggests that having more Chinese in one’s social network may be an inhibiting factor in acquiring an Australian-like categorisation, which would see the crossover from hit to het occurring at the acoustic midpoint. This finding supports the role of experience (i.e., exposure) in perception of vowel contrasts (Cutler 2012; Pierrehumbert 2002). For the /het-hat/ stimulus, between steps 1 and 5, the L2 listeners with fewer Australians in their social network had slightly more hat responses than het responses (more Australian-like), while for those with higher scores, responses were primarily conditioned by continuum step (with hat responses gradually increasing across the continuum). This shows little support for the effect of a higher Australian SNS for this stimulus.

Furthermore, any effect of proficiency in English (for the L2 listeners only) was only found in interaction with social network. For /hill-hell/ below Step 5, for those with IELTS ≤6.5, some of those with fewer Chinese people in their network displayed a tendency to respond slightly more with hell (more Australian-like), while those with more Chinese in their network responded almost categorically with hill (less Australian-like). For those with higher IELTS scores (>6.5), there was a small difference between those with fewer Chinese in their network, who provided a few hell responses at or below step 4 (more Australian-like), while those with more Chinese in their network all responded entirely with hill (less Australian-like). Overall, these nuances suggest that the perception of vowel contrasts is a complex task, most likely supported by experience and exposure with the L1, rather than proficiency. Perception can also be affected specifically by L2 vocabulary size, as found by Wang et al. (2023). As with L2 production, there are myriad factors that predict success in native-like acquisition (see Ellis 2008: Ch. 13 for an overview) that go beyond proficiency as measured in standardised tests. It should be noted, however, that the Chinese participants did have a high overall average proficiency in English, making it more difficult to measure the effect of e.g., low versus high proficiency in this study.