Individual differences in phonetic imitation and their role in sound change

1.1 Phonetic imitation and its role in sound change

Phonetic imitation is the process by which speakers tend to make their speech more similar sounding to that of the speaker they are interacting with. This process has sometimes also been called phonetic convergence, alignment or accommodation depending on the specific subfield of linguistics or theoretical paradigm, with sometimes subtle differences in what is precisely meant (see also Section 1.3.). For the sake of this study, we assume that these terms refer to the same process and use the term phonetic imitation. The idea that phonetic imitation plays a central role in the process of sound change can be traced back to the end of the nineteenth century (e.g., Paul 1880; Sievers 1901). It has been suggested that phonetic imitation is the seed for sound change, but also the mechanism (or one of the mechanisms) by which it is spread (e.g., Delvaux and Soquet 2007; Garrett and Johnson 2013; Pardo 2006; Trudgill 2004, 2008).

In the first case, imitation is viewed as the mechanism by which variation can result in sound change. Speakers can and do imitate, but they do so quite imperfectly (e.g., Babel 2012; Pardo 2006), they only approximate other speakers’ productions, which results in mutations of the signal and provide the innovations themselves. Imperfect imitation is often investigated in relation to language acquisition, with children as the main actors of change. Children, however, cannot be the only actors of change, since there is ample evidence that adult speakers participate in ongoing changes taking place in their community (e.g., Harrington et al. 2000; Sankoff and Blondeau 2007). In short, if imitation – either by children during acquisition or adults throughout their life – is the seed for linguistic change in general, phonetic imitation is the seed for a specific kind of linguistic change: sound change.

In the second case, phonetic accommodation is considered as one of the main mechanisms by which a change is disseminated through a community (Harrington and Schiel 2017; Trudgill 1986, 2008) as interacting listeners and talkers accommodate to each other’s speech patterns. Under the change-by-accommodation model (Auer and Hinskens 2005; Niedzielski and Giles 1996; Trudgill 1986), new variants are spread through the automatic adjustment of phonetic properties in response to an interlocutor. This model consists of three stages: (1) a short-term accommodation in individual interactions, (2) a long-term accommodation resulting in permanent changes in the speech of individual speakers, and (3) the spread of new variants throughout the community. Based on the transition from short-term temporary accommodation to long-term permanent accommodation, this hypothesis manages to relate ‘change at the level of the community to variable use in verbal interaction’ (Auer and Hinskens 2005: 356). This idea is rooted in the social psychological framework of Communication Accommodation Theory (CAT) (Giles and Smith 1979; Giles et al. 1973), where imitation behavior was considered as social acts that talkers use to modulate social distances in communication.

Delvaux and Soquet (2007) noted that these two views are not contradictory. Indeed, they depart from the observation that phonetic imitation occurring at the inter-individual level has to account for two apparently paradoxical features of the language system: the stability of phonetic realizations within a speech community and their potential for sound change. Delvaux and Soquet (2007) proposed that – when interacting – speakers and listeners automatically tend to converge and achieve a consensus on the acoustic-phonetic level, resulting in a hybrid realization. At the community level, the accumulation of these consensuses results in a particular set of variants characteristic for a particular language variety. Moreover, these consensuses are constantly renewed and the corresponding mental representations are updated. Sound change can then be defined as a consensus within a community to use a particular phonetic variant that is different from the one achieved previously (Delvaux and Soquet 2007). In this way, phonetic imitation could be both the cause of sound change and the mechanism of its spread.

More generally, it had widely been assumed that sound change indeed relies on two distinct events that occur sequentially: first, the creation of a new variant in speech variation (called innovation or actuation (Weinreich et al. 1968)), and second its propagation, the spread in the speech community. This conceptualization of sound change in terms of a two-step process of variation and selection draws inspiration from biological evolution (Yu 2013). As pointed out by Labov (1994) however, we do not necessarily need to distinguish between these two processes, as it is peculiar to think of sound change relying on two distinct activities: a sort of creative act of deviating from the existing use (of which it is unclear whether it is even observable) (Croft 2000), followed by the imitation of this innovation by other speakers. We assume that sound change is much more of an iterative process at the individual level in which minimal changes incrementally accumulate in a speaker’s system every time he speaks to a listener (as an intrinsic result of phonetic imitation). Under such a theoretical assumption, sound change might be defined as a purely synchronic process, residing in the variability inherent to production and perception. The propagation of a change is mostly observable when comparing groups of speakers (stratified by age for instance or any other broad social category). A sound change is ‘actuated’ every time again in new speakers and new communities. Diachronic change is then merely a series of incremental acts of actuation in a defined direction.

1.2 The individual versus the group level

Based on these insights about the role of imitation in change, it is interesting to examine how and why sound change spreads through space. If we consider a language area to consist of smaller regional communities, the consensuses achieved in a region might logically slightly differ from the consensuses in other regions, since there is more inter-personal contact within than between regions. As these consensuses are constantly renewed and updated, sound changes can spread through the language area.

Dialectologists have mostly been concerned with this geographical dimension of sound change, and thus with the group level. Sociolinguists who study change over time have also been interested in the question of who leads language change (Labov 2001; Tamminga 2021). Their research has extensively shown that the advancement of a sound change is not uniform in one single community (e.g., Harrington and Schiel 2017; Milroy 2002; Milroy and Milroy 1985). Even in a small local community sound change is not equally advanced in each member, leading to a puzzling question: where do these individual differences come from? Various studies have aimed to identify speakers who are at the forefront of ongoing sound change, those often called leaders of change, innovators or early adopters (with some subtle differences between these terms). To date, many characteristics of the leaders of change have been revealed, most of them related to social factors in Western societies, such as network density and size (e.g., Lev-Ari 2018; Milroy 1987), gender (e.g., Eckert and McConnell-Ginet 2003; Labov 1990, 2001) or social class (e.g., Ash 2002; Chambers 2002; Labov 2001). More recently, the possibility that this lack of uniformity in the spread of a change within a single region or community is related to individual differences in linguistic, social and cognitive aspects has been proposed. Baker et al. (2011) looked at /s/-retraction in American English and Beddor (2012) at coarticulation patterns. They proposed that individual speakers differ in the extent to which they respectively retract versus coarticulate and therefore do not all participate to the same extent in the sound change. Garrett and Johnson (2013) proposed that some speakers are more likely to index linguistic differences with social meaning than others and that this difference in indexicality is a driving force in sound change. Yu (2013) explored individual variability in cognitive processing in relationship to the spread of sound change throughout a community. He found that the personality and social profiles of some individuals (more extroverted and agreeable) can predict the spread of sound change in their networks. Tamminga (2021) undertook a preliminary investigation of leadership and personality traits in order to predict the spread of a set of sound changes in Philadelphia English. Based on her results, she showed some pessimism towards the idea that such traits can predict individual differences in change advancement. The current study primarily aims to bring these attempts further by exploring to extent to which individual differences in change advancement can be linked to phonetic imitation capacities.

1.3 The types of phonetic imitation

In sociophonetic research different paradigms have been developed to examine phonetic imitation (Pardo 2013). The shadowing paradigm was first introduced into the field by Goldinger (1998) and subsequently used in numerous imitation studies. In a shadowing study, participants typically listen to and repeat isolated words or sentences. Results within this paradigm have repeatedly showed that subjects shift their speech production in the direction of speech they are asked to repeat. These shifts are constrained by linguistic factors (e.g., Nielsen 2011) and modulated by language-external factors, which result in significant variability between speakers in the extent and the directionality of shifts (convergence vs. divergence patterns). Factors such as attitude toward the interlocutor (Abrego-Collier et al. 2011), gender both of the speaker and listener (Babel 2010; Namy et al. 2002), personality traits (Yu et al. 2013) and cognitive load of the task (Abel and Babel 2017) and have been shown to constrain these phonetic imitation patterns.

Additionally, efforts were made to examine phonetic imitation involved conversational interaction, a setting where imitation is traditionally thought to be more relevant than within the non-interactive shadowing task (see Communication Accommodation Theory in Giles and Smith 1979 and Giles et al. 1973). Delvaux and Soquet (2007) designed a task in the lab with a higher degree of social interaction than in previous studies. They observed what they called deliberate imitation: when simply exposed to the other regiolect, speakers of one regiolect produced vowels that were significantly different from their typical realizations, and significantly closer to the other regiolect. Pardo et al. (2018) compared phonetic convergence in conversational interaction and in a non-interactive speech shadowing task and found that patterns of phonetic convergence highly differed across these settings. Sonderegger et al. (2017) managed to investigate imitation outside the lab by looking at the recordings of the reality television show Big Brother. Speakers participating in the show were locked up together in an isolated house for three months. Their methodology also allowed to focus not only imitation in the short term, but to explicitly look at the medium term (i.e., a few month). They looked at five phonetic variables and found that small daily accent fluctuations were ubiquitous, while more persistent accent changes occurred only in a minority of speakers.

These attempts to examine phonetic imitation in more spontaneous, non-laboratory and interactional settings focused on language variation. Yet, it remains crucial to compare synchronic variation patterns to the specific situation of sound change. Especially in the context of sound change, it is interesting to investigate not only whether people are willing to imitate, but also whether they are capable of imitating. By definition, sound change means that the present and future realizations of a variable are different than the original, past realizations. Some speakers might at some point in the change lose the ability to produce a sound is a certain way. Therefore, it appears important in the case of sound change to consider both the (sociolinguistic) willingness or readiness and the (phonetic) articulatory or auditory capacity to imitate.

To the best of our knowledge, only Dufour and Nguyen (2013) have investigated what we can called forced and spontaneous imitation separately. Using a stable vowel variable in French, they compared the phonetic convergence effect observed in − on the one hand − an imitation task in which participants were explicitly instructed to imitate the productions they were exposed to (forced imitation), and on the other hand − a shadowing task which was meant to trigger unintentional imitation (i.e., spontaneous imitation). They found that the phonetic convergence effect was greater when participants intentionally imitated the speaker’s productions than in the shadowing task, and that both types of instruction led to the same degree of convergence in a post-exposure task.

The goal of this study is to answer the question to what extent these two different types of imitation play a role in the case of a sound change, and what their respective contribution is to the spread of a change. We hypothesized that the speakers who tend to easily imitate spontaneously might introduce the change into their own community, while speakers who fail to imitate when instructed to, take a conservative position in the change: they keep on reproducing the existing speech patterns in a precise way, and in doing so, reinforce the stability of phonetic realizations within the community. In conclusion, we expect that there is a correlation between speakers’ innovativeness in sound change and their spontaneous imitation capacities on the one hand, and between speakers’ conservatism in the change and their forced imitation capacities in the other hand.

1.4 This study

In the current paper, we explore individual differences in imitation capacities for a Dutch variable that is involved in the process of sound change. The devoicing of labiodental fricative /v/ is a well-attested ongoing sound change in the Dutch language area, showing regional and individual variation (see Section 1.5.). Those patterns are investigated in a baseline production task (Experiment 1). Subsequently, we explore imitation capacities in a forced imitation task (Experiment 2) and a spontaneous imitation task (Experiment 3) and the extent to which the imitation results relate to individual differences in change advancement. Unlike the order in which the data are presented in this paper, participants conducted the tasks in the following order: the baseline production experiment, the spontaneous imitation experiment and finally the forced imitation experiment. The order permitted to avoid that the forced imitation task, being inherently meta-linguistic, influenced the spontaneous imitation task.

1.5 A sound change in progress: the devoicing of labiodental fricatives in Dutch

Standard Dutch is traditionally described as having a phonological distinction between voiced and voiceless fricatives. The major cue for the voiced/voiceless distinction is the presence or absence of vocal fold vibration in the fricative (Slis and Cohen 1969). During the last decades, it has been frequently observed that word-initial voiced fricatives in standard Dutch are increasingly produced as voiceless (Cassier and Van de Craen 1986; Cohen et al. 1961; Gussenhoven 1999; Hamann and Sennema 2005; Kissine et al. 2003; 2005; Mees and Collins 1982; Van de Velde 1996; Van de Velde et al. 1996; van der Wal et al. 1992).

Regional differences are observed in the devoicing of voiced fricatives. Slis and van Heugten (1989) found stronger devoicing in the West of the Netherlands than in the South. Van de Velde et al. (1996) showed in a real-time study that fricative devoicing is a rapidly advancing change in progress in the Netherlands, and found the first signs of fricative devoicing in Flanders. In a follow-up study, these insights were refined by focusing on regional differences within the Netherlands and Flanders and on the /v/-/f/ contrast (Kissine et al. 2003, 2005). They found that West-Flanders is the most conservative region, showing the highest scores for voicing of /v/, and that the North of the Netherlands is the most advanced with almost complete devoicing. Other regions exhibited intermediate states. In conclusion, this sound change shows significant regional variation and can be considered as advanced, but not completed at the moment we are conducting this study. Pinget et al. (2020) showed that the regional differences in the amount of devoicing in fricatives matched the patterns described in previous studies and appeared to be even further advanced than reported two decades ago by Kissine et al. (2003, 2005. They also showed that there was a clear link between the production and perception systems undergoing sound change.

2.1 Method

2.1.1 Regions and participants

Based on the studies described in Section 1.5, five regions within the Dutch language area were chosen to reflect different stages of fricative devoicing: West-Flanders (WF), Flemish-Brabant (FB), Netherlands Limburg (LI), South-Holland (SH) and Groningen (GR). These regions were selected to represent smaller communities within the larger Dutch language area. They are represented on the map in Figure 1.

Figure 1:

Map of the Dutch language area (The Netherlands and Flanders only) and of the five selected regions. Each dot represents the origin of one or more participants (n = 20 per region).

West-Flanders (WF) is a peripheral region to the West of Flanders along the North Sea. The chosen area is situated around the towns of Kortrijk and Roeselare. This region is known to be the most conservative in terms of fricative devoicing (Kissine et al. 2003, 2005). Flemish-Brabant (FB) is the central area in Flanders, having a comparable economic, cultural and political status in Flanders to South-Holland in the Netherlands. Weak patterns of fricative devoicing have been found in this region (Kissine et al. 2003, 2005). Netherlands Limburg (LI) is a geographically peripheral region situated in the South of the Netherlands, stretching from Venlo to Maastricht. The fricative devoicing process is weak to moderate in this region (Kissine et al. 2003, 2005). South-Holland (SH) is part of the Randstad, the central area in the Netherlands consisting of the urban zone in the western provinces North-Holland, South-Holland and Utrecht. The chosen region centers around the towns of Leiden and Delft. Production studies have shown strong devoicing of fricatives in this region (Kissine et al. 2003, 2005) and it is often considered as the area from which this phenomenon is spreading (Van de Velde et al. 1996). The Groningen region (GR) is situated in the North of the Netherlands and is centered around the cities of Groningen and Assen. It is known to be a region where the voiced/voiceless fricative contrast has almost completely faded, resulting in a merger (Kissine et al. 2003, 2005).

The participants were one hundred native speakers of Dutch born and raised in these five regions. Of each region, 10 males and 10 females took part in the production experiments. Participants were all highly educated young adults aged between 18 and 28 years (mean = 22.03 years). All participants were attending or recently graduated from a university or college, and were fluent speakers of colloquial standard Dutch. No participant reported having any hearing or speaking problems.

2.2 Results

Voicing measures for /v/ and /f/ (in %) split up by region are presented in Figure 2. First, we observed that voicing measurements for /f/ are very stable across regions and individuals, while regional and individual differences in the realization of /v/ are very large. As expected, West-Flemish participants produced /v/ with the highest degree of voicing (mean = 56.64% of voicing). Flemish-Brabant (mean = 49.84%), Limburgian (mean = 43.02%) and South-Hollandish voiced fricatives (mean = 29.64%) show gradually more devoicing in this order. Groningen participants produced the most devoiced /v/’s (mean = 19.85%) with values not different from /f/’s: they have a merged /v/ and /f/ production. There are however two speakers in Groningen who produced /v/ with much more voicing than the other speakers of the same region.

Figure 2:

Boxplot of voicing measures (in %) for /v/ (in grey) and /f/ (in white) in the aggregated data (one value represents one speaker), split up by region.

Altogether, these results confirmed previous work on the devoicing of labiodental fricatives: it is an advanced sound change showing regional stratification. The devoicing is resulting in a merger: /v/ is merging into /f/, and not the other around. This merger is the most advanced in the regions of South-Holland and Groningen.

Furthermore, there are large individual differences in the production of /v/ within each region. In other words, the spread of the change is not uniform within the different regions. The amount of voicing in /v/ realizations were fit with a linear mixed effects model using the lme4 package in R (Bates et al. 2015). The model intends to control the effects of a range of linguistic and social predictors that are not the target of this individual differences investigation, and which were included as fixed-effects predictors: (1) speech style (i.e., word reading, carrier sentence reading, sentence reading, semi-spontaneous speech, spontaneous speech), (2) region (West-Flanders, Flemish-Brabant, Limburg, South-Holland, Groningen), (3) speaker gender (male, female) and (4) speaker age (at time of interview). The model also included by-word and by-speaker random intercepts.^[1] Following a procedure previously used by e.g., Drager and Hay (2012), Voeten (2021) and Tamminga (2021), we take the by-speaker random intercepts as a measure of how innovative or conservative a speaker is in comparison to the other speakers in the dataset within their own region, controlling for the other social and linguistic factors. This procedure provides a determination of every speaker's place on the innovativeness continuum. When random intercepts are positive, speakers are more conservative in the change than their peers. Producing more voicing is thus conservative in the case of a devoicing process. A zero intercept means that the speaker’s average voicing production is equal to the regional mean. A negative intercept indicates that the speaker is more innovative than the peers. This measure of speakers’ innovativeness will in the subsequent part of the paper be used to compare the advancement in the sound change with imitation capacities (forced imitation in Experiment 2 and spontaneous imitation in Experiment 3).

3.1 Method

3.2 Results

3.2.2 The use of voicing and its relationship to the advancement of the sound change within individual speakers

In this section, we focus on the use of voicing as a cue in the forced imitation task and how the use of this cue relates to the advancement of the sound change at the individual level. First, the range in voicing that individual speaker produced in the imitation task was investigated. For each participant, the voicing range between the most voiced and most voiceless realizations in the imitation task was computed, and was expressed between 0% (no range) and 100% (full voicing range). Most participants were able to produce a voicing range of (nearly) 100%. However, a few participants did not achieve high voicing ranges. Six participants showed a voicing range around 50% and four participants showed ranges lower than 25%. These participants mostly failed to produce enough voicing when imitating the voiced stimuli.

Secondly, the accuracy of the forced imitation was examined. For each trial in the forced imitation task, the amount of voicing in the target fricatives produced by the model talker was subtracted from the amount of voicing produced by the speaker. This measure is called voicing accuracy. Whenever voicing accuracy is positive, there is overshoot: the speakers produced too much voicing compared to the model talker they were instructed to imitate. A negative score indicates voicing undershoot: the speaker failed to produce enough voicing compared to the token produced by model talker. Zero in voicing accuracy represents a perfect imitation.

We fitted a linear mixed effects model using the lme4 package in R (Bates et al. 2015) with the speakers’ degree of innovativeness (see Section 2.2) and the total voicing range (i.e., across the whole forced imitation task) as predictors for the voicing accuracy. The model also included by-speaker and by-word random intercepts.^[2] There were significant main effects of the degree of innovativeness (ß = 0.379, t = 3.679) and of the range innovativeness (ß = 0.416, t = 5.127), reflecting the facts that more innovative in the change, the more speakers undershoot during forced imitation and the smaller the voicing range they can produce.^[3]

These effects are visualized in Figure 3, with on the x-axis the speaker’s degree of innovativeness within their own region (based on the baseline production task) (see Section 2.2) and the y-axis the voicing accuracy in the forced imitation task, split up by voicing range in gradient color.

Figure 3:

Scatterplot of voicing accuracy in the forced imitation task as a function of the speaker’s degree of innovativeness within their own region (based on the baseline production task). Each symbol represents a trial in the forced imitation task (n = 2468), the gradient color indicates the total voicing range.

4.1 Method

4.2 Results

Firstly, the extent to which individuals converge to or diverge from the model talkers was investigated. For each speaker, a linear regression was fitted to the 30 /v/ trials with the order of trials as predictor. In each model, a slope significantly different from 0 indicates a gradual imitation pattern: either convergence when the slope is negative or divergence when the slope is positive slope. Figure C in Appendix 2 shows an example of individual patterns of convergence. Six participants from a total sample of 60 showed a significant slope in the regression models: four slopes were significantly negative with the speakers spontaneously converging towards the model talkers, while two slopes were significantly positive with speakers diverging from the models. For 54 participants, the order of trials as predictor had no significant effect on the amount of produced voicing.

Secondly, we examined to what extent speakers’ /v/ realizations were influenced by the model talkers. Importantly, the distances that each speaker has to cover in order to converge towards the model talkers necessarily depends on their baseline production. We therefore subtracted the individual baseline production of /v/ as obtained in Experiment 1 from the /v/ realizations in the spontaneous imitation task at the by-talker and by-trial level. The obtained scores represent the modification in voicing of /v/ induced by the exposure to the model takers. A positive modification score means that /v/ productions in the spontaneous imitation task are more voiced than the speaker’s baseline production. A negative modification score means that /v/ productions in the spontaneous imitation task are less voiced compared to the baseline, that is what we expect as the model talkers have a highly devoiced /v/. Zero means that the speaker’s production was not influenced by the model talker. We fitted a linear mixed effects model using the lme4 package in R (Bates et al. 2015) with the speakers’ degree of innovativeness (see Section 2.2.) as predictor of the amount of voicing in the imitated /v/ realizations. The model also included by-speaker and by-word random intercept.^[4] There was a significant main effect of the degree of innovativeness (ß = 0.406, t = 2.512), showing that more innovative in the change, the more speakers show a voicing modification: their /v/’s became less voiced compared to the baseline as a result of exposure to model talkers.

This effect is visualized in Figure 4, with on the x-axis the speaker’s degree of innovativeness within their own region (based on the baseline production task) (see Section 2.2) and on the y-axis the voicing modification induced by the model talkers, with different shapes depending on the general gradual imitation pattern over the time course of the experiment.

Figure 4:

Scatterplot of voicing modification induced by the model talkers in the spontaneous imitation task as a function of the speaker’s degree of innovativeness within their own region (based on the baseline production task). Each symbol represents a trial in the spontaneous imitation task with an indication of the general gradual imitation pattern, either no significant pattern (open circle), a pattern of convergence towards the model talkers (filled circle) or a pattern of divergence from the model talkers (filled triangle) (n = 1779).

The fitted line visualizes the main effect found in the model, whereby the most conservative speakers in each region (with a positive degree of innovativeness) showed no voicing modification as compared to their baseline (around 0), while the most innovative speakers (with a negative degree of innovativeness) showed a lower voicing in /v/’s produced in the imitation task compared to their baseline, as a result of exposure to the model talkers with high degree of devoicing. Moreover, we observed that the four speakers who showed a significant tendency to gradual convergence towards the models are all quite innovative in their own region (to the right side of the x-axis), which confirms the relationship between the readiness to imitation and the advancement in the change.

4.3 Intermediate discussion

This spontaneous imitation experiment aimed to investigate participants’ readiness to use the devoiced variant of labiodental fricatives in a social interaction.

First, it turned out that a small proportion of participants (10%) showed significant gradual imitation pattern of convergence versus divergence in the course of the experiment. Speakers who gradually converged were all relatively innovative in the change. This small proportion of spontaneous imitators is similar to what was found in similar previous studies (Sonderegger et al. 2017). The assumption was here that the task gradually triggers more imitation with more exposure to the model talkers. However, it only contained 30 trails of the target variables (apart from the practice trials and the distractors). It cannot be excluded that some speakers who did not imitate in the task would have needed more time and exposure (i.e. more trials) to converge to the model talkers.

Furthermore, it was examined whether the modification in voicing of /v/ in the imitation task compared the baseline could predict the position of speakers in their own region. It turned out that the more advanced speakers in their own region, the more they were inclined to imitate the model talkers. Leaders of change within a community seemed to be more ‘willing’ to devoice (even more than what they normally do), while more conservative speakers are less inclined to spontaneously imitate model speakers producing the new variant. In conclusion, the task succeeded in triggering – for most speakers – phonetic convergence (at least at the token level) through simple exposure to two model speakers, even if the social situation was minimally interactive (as compared to usual social situations in which one plays card games).