Vowels in urban and rural Albanian: the case of the Southern Gheg dialect

1.1 Albanian and Gheg

Albanian (shqip in Albanian) is a language of the Indo-European family spoken by 6–7 million people (Rusakov 2017) who live mostly in Albania and Kosovo, but also in North Macedonia, Italy, Greece, Montenegro and Serbia (see Figure 1). It is widely accepted that Albanian forms a branch of its own within Indo-European (e.g. Bopp 1855; Çabej 1976).

Figure 1:

Main Albanian-speaking areas (excluding recent diaspora) in pale and dark gray. Albanian is spoken by the majority of citizens only in Albania and Kosovo.

In this paper, we are concerned with Albanian spoken in Albania, which has traditionally been described as comprising two main dialects: Gheg, spoken in northern and central Albania, and Tosk, spoken in the south of the country, with the Shkumbin river roughly marking the divide between the two (Beci 1965, 1970a, 1970b, 1987; Byron 1976; Çabej 1936, 1976; Desnickaja 1968; Gjinari 1963, 1988; Hahn 2013; etc.). Gheg and Tosk differ in several lexical, morphosyntactic, phonological and phonetic features (e.g. Beci 2002, 2019; Çabej 1936, 1964; Dozon 1879; Gjinari 1963; Lambertz 1948; Luboteni 1960a, 1960b; Pekmezi 2020; Topalli 2007), some of which are historically motivated (Çabej 1958, 1964, 1988; Demiraj 1981, 1996; Jokl 1932; Riza 1982; Topalli 2005, 2007; de Vaan 2018; Vermeer 2008). For example, Gheg contrasts oral and nasal vowels, e.g. /pi/ ‘drink (imperative, second pers. sing.)’ versus /pĩ/ ‘numb (imperative, second pers. sing.)’ (Beci 1995), while the phonological inventory of Tosk only comprises oral vowels (the contrast above is realized as /pi/ vs. /mpi/ in Tosk). Another example at the morphosyntactic level is that Gheg contains infinitives, e.g. me ba ‘to do’, while Tosk shows an innovative replacement of infinitives by finite forms, e.g. për të bërë ‘to do’ (Gjinari 1966). An example of a lexical difference is the noun for ‘rooster’, which is kaposh in Tosk and gjel in Gheg (Gjinari 1966).

Regional variation within Gheg and Tosk has been previously documented (e.g. Beci 1995; Çeliku 1965, 1966, 1968; Gjinari 1988; Gjinari et al. 2007), as can be seen in the dialect subdivisions presented in Figure 2. Tosk has traditionally been described as having two subvarieties, i.e. Northern Tosk and Lab (Southern) Tosk, while Gheg comprises four different subvarieties, i.e. Northeast Gheg, Northwest Gheg, Central Gheg and Southern Gheg. Our focus in this paper will be on Southern Gheg, spoken in the central part of Albania, where the capital city Tirana and the village of Bërzhitë (within the district of Bërzhitë) are located.

Figure 2:

Dialect areas of Albania, based on divisions from Gjinari (1988).

In addition to Gheg and Tosk dialects, Albania has a standard variety, which was established in 1972 (Section 1.2). Standard Albanian is taught in schools all around the country and is spoken countrywide in public venues such as media, governmental institutions and schools, but, in contrast to the (sub)dialects, it is not associated with a particular geographical area. The standard is structurally much more similar to Tosk dialects than to Gheg dialects (Byron 1976; Moosmüller and Granser 2006). Regarding the features this article is concerned with, there is no difference between Tosk and the standard, which will henceforth be referred to as Tosk & Standard.

The set of phonological and phonetic features we will be focusing on is as follows (see Sections 2.1–2.3 for more details): 1) in Southern Gheg, the stressed low vowel /a/ has two contextual variants, rounded and unrounded, depending on whether the preceding consonant is nasal or not, while Tosk & Standard and other varieties of Gheg exclusively have the unrounded variant of /a/ across phonetic contexts; 2) Gheg has phonological vowel length contrasts, among other contexts, between definite and indefinite nouns, while Tosk does not contrast length anymore (except in southern Labëria, an area not of focus here), and neither does the standard; 3) where Tosk & Standard contrast monophthongs and diphthongs/vowel sequences thought to have emerged historically after high vowels were lengthened by word-final liquids, Southern Gheg^[1] has not undergone this change and has monophthongs only. To summarize, our main aim is to understand how these three features have been influenced by the Tosk-based standard variety and contact with Tosk speakers brought about by a series of sociopolitical events that will be described in the following section.

1.2 Socio-political changes in Albania and portrait of Tirana and Bërzhitë

Geographic, demographic and historical facts unique to each setting have been highlighted as important for understanding dialect change (e.g. Bailey et al. 1996; Dodsworth 2017; Labov 1994; Trudgill 2020). In the case of Albania, three such specific events can be identified: the adoption of the Tosk-based standard, state-directed migration during the communist era, and rapid urbanization after the fall of communism. These will be reviewed in turn, after which we will explain how the populations of Tirana and Bërzhitë have been affected differently by these events, thereby justifying why these two locations were chosen to represent the urban/rural divide in this study.

First, standard Albanian emerged from a strict language planning policy, or as described by Byron (1976: 13), “systematic, government-sponsored standardization.” It was meant to fulfill the communist political agenda of unifying the country under the umbrella of one language, in line with Marxist theory (e.g. Kostallari 1973, 1984). The standard was adopted in 1972 during the National Congress of Orthography of Tirana and was mainly based on Northern Tosk (see Figure 2), although a small number of Gheg features were also adopted, principally lexical ones (e.g. krenar ‘proud’, synoj ‘to aim’; Gjinari 1973). Both the orthographic system of the standard and the phonological system on which the orthography is based thus exclude most features of pronunciation specific to Gheg, e.g. contrastive vowel length or nasality. From 1972 onwards, such dialectal variants that differed from the standard were to be avoided in writing, education, public speaking and the media (Ismajli 2005; Kostallari 1973, 1984), and were thereby relegated to local and family use only. In other words, even though the standard is not a mere copy of Tosk, the number of features that Gheg speakers have to adapt to when using the standard is disproportionately higher than for Tosk speakers.

Second, during the communist era (1946–1992), migration was not a human right, but a process controlled by the state under which Albanian people were assigned where to live based on the country’s needs and policies (Gedeshi and Jorgoni 2012; Misja and Vejsiu 1990). Part of the regime’s vision of urban planning was to create or expand large industrial facilities (e.g. Çaro 2011; Lerch 2016). For this purpose, workers and their families were brought from all areas of Albania to the developing industrial centers in order to support the country’s industrialization. Bureaucrats, army officers and other professionals were also commonly rotated and moved from one location to another (Vaqari 2020). In parallel, the regime had a rural retention policy that strongly restricted rural-to-urban migration in order to strengthen the agricultural sector (Gedeshi and Jorgoni 2012; Lerch 2014; Sjöberg 1994).

Third, after the collapse of communism in the 1990s, migration was effectively legalized as a human right in the Constitution of the Republic of Albania (Vullnetari 2014) and Albanians regained the right to choose their place of residence and to move freely around the country and/or abroad. This led to a substantial internal migration flow to urban centers, during which people left rural areas in search of better life and economic opportunities (Carletto et al. 2006; Institute of Statistics [INSTAT] 2015; Lerch 2016; Vullnetari 2014). This phase of rural exodus that further transformed cities into contact hubs and in which the rural areas received scarcely any newcomers is fairly common worldwide (see e.g. Auer 2018). However, the combination of two consecutive migratory flows of a rare intensity, the first state-orchestrated and the second voluntary, resulted in unique population movements that make Albania stand out among the Balkan and European countries (see e.g. King et al. 2011; Lerch 2014; Vullnetari 2014).

The capital, Tirana, is of particular interest regarding the three aforementioned socio-political events: the adoption of the Tosk-based standard, state-managed migration and rapid urbanization. First, as the main administrative, educational and media hub, Tirana is arguably the place in Albania where the standard is the most spoken on a daily basis. Since 1972, its Southern Gheg speaking community has thus been in sustained and direct contact with this Tosk-based variety. Second, Tirana was one of the industrial centers that was expanded under communism, with large plants such as the Textile Factory, the Tractor Machine Production Facility, the Prefab Building Materials Industry, etc. established there. It was also a place where various bureaucrats would be more or less temporarily stationed. State-managed migration precipitated social and linguistic mixing in these centers (Beci 1987; Gosturani 1983a, 1983b, 1988; Osmani 1985; Shkurtaj 1984; Totoni 1966); in the case of Tirana, the local Southern Gheg community came to be in intense contact with migrants from both Tosk-speaking areas and other Gheg-speaking areas. Third, since the fall of communism, the population of Tirana has tripled, from approximately 250,000 to 800,000 inhabitants (Gjonça et al. 2015; Halilaj 2021), with people from Tosk-speaking, Gheg-speaking, rural and other urban areas moving in. In sum, the social and linguistic pressure brought upon the Southern Gheg speakers of Tirana during communism was further exacerbated when Albanians from all over the country started to flock to the capital.

The village of Bërzhitë comprises approximately 1,000 households (INSTAT 2013) and is located 15 km from Tirana. Despite this geographical proximity, Bërzhitë preserves its rural character and a fairly traditional way of life. For instance, as per the 2011 census, 6% of the inhabitants held a university degree, 82% of the households used woodfire as their main heating source, 23% of the households did not have access to toilets in their direct living space, 10% of the households owned a computer and only three households owned a dishwasher (INSTAT 2013). Bërzhitë also stands in sharp contrast to the capital regarding the effects of the three socio-political events identified. First, villagers are in contact with the Tosk-based standard mainly during their schooling hours and later on through official communication channels. Their degree of exposure to the standard is not negligible, but it is more passive and less direct than that of the inhabitants of Tirana. Second, Bërzhitë was virtually unaffected by the migratory turmoil of the communist era: it was not an expanding industrial and administrative center receiving workers from all over the country like Tirana, and the rural retention policy limited population movements. This was confirmed during field trips through conversations that one of the authors had with the older participants and the school principal wherein it was explained that villagers were required to stay put and work in the tobacco and olive cooperatives nearby. Third, as most rural areas across Albania, Bërzhitë has seen emigration over the past 30 years, but barely any immigration. For instance, while the population of Tirana is still booming, the district of Bërzhitë lost 47 inhabitants in 2020 (50 new arrivals and births vs. 97 departures and deaths), for a population of nearly 6,000 inhabitants (Tirana Municipality 2021). Furthermore, the lack of proper roads and the presence of the Erzeni river between Tirana and Bërzhitë have long prevented the two populations from frequently interacting (Çeliku 2020). To this day, Bërzhitë still does not have a direct connection to the newly constructed highway leading to Tirana and it is not a (dormitory) suburb despite its geographical proximity to Tirana. Very few locals commute to the capital for work and most of them live off their land.

To summarize, at least three socio-political events in the recent history of Albania have contributed to creating an urban/rural divide likely to affect language use. Urban and rural speech communities alike are exposed to the Tosk-based standard established in 1972, but urban speakers appear to be in more direct contact with it, which may have created an urban/rural divide in Gheg speaking areas (but less so in Tosk speaking areas given the linguistic proximity between Tosk and the standard). State-directed migration, then free internal migration patterns have led to sustained population mixing and dialect contact in urban settings, while the population of rural areas has experienced no such contact. The capital city Tirana and the nearby village of Bërzhitë, both located in the Southern Gheg speaking area, have been selected to represent urban and rural settings respectively in this study of how the vocalic system of Southern Gheg is influenced by the standard and contact with Tosk speakers in urban versus rural settings.

1.3 Dialects in contact

Dialects in contact are recognized as one of the driving forces behind language variation and change (Britain 2009, 2018; Trudgill 1986, 2008). That is to say, repeated interactions between speakers who have no difficulties understanding each other have often been identified as the likely cause of changes in spoken accents and dialects over time (see Britain 2018; Dodsworth 2017 for recent reviews). There are various settings in which dialect contact may occur: for example, massive influxes of immigrants relative to the size of the local population (Kerswill and Williams 2000), the colonization of a territory (Trudgill 2008), scientists living together in Antarctica (Harrington et al. 2019), or participants in a reality TV show (Sonderegger et al. 2017). However, densely populated urban centers (Dodsworth 2017) are among the most frequent causes of dialects in contact. These create the most conducive conditions for various groups to live in close proximity, mix and interact daily. For this reason, dialect change is expected to be more likely to occur in Tirana than in Bërzhitë.

There are two main frameworks which account for why speakers in contact come to influence one another: one based on social motivations and one based on automatic mechanisms (see Coles-Harris 2017; Siegel 2010 for reviews). The current study falls within the second framework, where change is seen as an automatic, unconscious and inevitable process (Chartrand and Bargh 1999; Delvaux and Soquet 2007; Harrington et al. 2018; Labov 2001; Pickering and Garrod 2004; Todd et al. 2019; Trudgill 2008) rather than an attempt to position oneself within the social structure, even if it does take place during social interactions and may occasionally be mediated by indexical factors (Babel 2012).

Individuals in dialect contact may change their productions due to convergence or imitation in speech communication (e.g. Babel et al. 2014; Nielsen 2014; Trudgill 2008): speakers constantly attune to each other in both perception and production. According to exemplar-based models (Bybee and Beckner 2010; Johnson 1997; Pierrehumbert 2002, 2006; etc.), this attunement has the potential to cause changes because the speakers-listeners’ cognitive representations of sounds are updated as new exemplars are stored (Drager and Kirtley 2016; Foulkes and Docherty 2006; Hay et al. 2006; Nycz 2013; Pierrehumbert 2006). Exemplar recency is thought to play an important role during interactions, with recent ones more likely to be imitated (Hay et al. 2010; Johnson 1997; Sancier and Fowler 1997). In such models, individuals may change their speech over their lifespan (e.g. Bowie and Yaeger-Dror 2015; Harrington et al. 2000; Sankoff 2018) as a consequence of their daily interactions, for instance with speakers of other varieties whom they listen to and imitate.

However, there are limits to the scope of imitation in adulthood, partly due to a certain loss of cognitive malleability after puberty (Lenneberg 1967). One of the outcomes is that adults inconsistently adopt contact-induced change, e.g. they might learn a phonological rule partially and apply it to a subset of relevant words only, or produce intermediate variants, or alternate between old and new variants. Within exemplar-based models, it has been suggested that exemplars memorized in the past were quite robust or entrenched (Harrington and Reubold 2021; Howell et al. 2006; Reubold and Harrington 2018; Walker 2018); they would weigh especially heavily in cognitive representations of sounds, even more so when combined with declining cognitive abilities that impede the updating of representations with recent exemplars. Moreover, while recent exemplars are readily imitated in the immediate term, their overall statistical weight may be reduced when recency fades and they are stored along a distribution that already comprises a large number of exemplars, i.e. stable distributions formed by years of exposure. In other words, the variants imitated in a given situation are not necessarily reproduced later on. These different components of exemplar models account for why the early stages of a change taking place among adult speakers are characterized by variability within and across individuals (cf. Labov’s 2007 concept of change through diffusion).

Contact can influence child speech in different ways. Children are known for their remarkable capacity to learn an entire linguistic system (sometimes more than one at once) at a very quick rate and from minimal exposure, in a way that adults cannot (e.g. Clark 2009; De Houwer 1996; Hartshorne et al. 2018; Kerswill 1996; Lenneberg 1967; Pinker 1996). Regarding speech acquisition, children are typically able to produce phonologically correct and recognizable speech by the age of 3 years old (e.g. Donegan 2013; Foulkes and Vihman 2015; Schölderle et al. 2020; Stoel-Gammon and Herrington 1990), though it is refined well into adolescence with the maturation of cognitive abilities and motor control (see e.g. Beckman et al. 2014; Lee et al. 1999; Vorperian and Kent 2007). During acquisition, children also have to deal with patterns of variation that are tied to social factors (De Vogelaer et al. 2017; Johnson and White 2020; Nardy et al. 2013; Smith 2021). Emergent sociophonetic variation has been documented in the speech of children aged 3–4 years old (Roberts 1997; Smith et al. 2007), while expectations about the social properties of the speaker have been shown to influence speech processing in 16-month-old infants (Weatherhead and White 2018; see also Weatherhead et al. 2016). The input that children receive is fundamental for them to acquire sociophonetic variation (Hendricks et al. 2018; Payne 1980) because they “cannot learn variation that they are not exposed to” (Johnson and White 2020: 6).

In situations of dialect contact where adults inconsistently adopt change (Labov 1994, 2007; Sankoff and Blondeau 2007), children’s input is irregular, including at home, since their caretakers likely participate in community change. Various researchers have observed in such situations that children’s great capacity to generalize from exposure, sometimes leading to mistakes like *choosed instead of chose due to overgeneralization of the -ed morpheme in English past tense verbs, turned adult variability into regularity (e.g. Austin et al. 2022; Cournane 2019; Hudson Kam and Newport 2005; O’Shannessy 2019; Sanz-Sánchez and Moyna 2022). Children have been considered to be instrumental in certain types of language change, for instance koine and creole formation (DeGraff 2009; O’Shannessy 2019; Senghas et al. 2004; Trudgill 2004, 2008).

On the other hand, children are not an influential group within the social structure (Aitchison 2002; Kerswill 1996), and they are known for their mistakes and idiosyncrasies which the rest of the community does not adopt, e.g. *choosed (Foulkes and Vihman 2015). Young children are also less likely to be instigators of sociophonetic change if they have not yet learned the association between phonetic and social variation (e.g. Barbu et al. 2013; Chevrot et al. 2011; Foulkes and Docherty 2006; Nardy et al. 2013). Moreover, variation in childhood may be modified into adolescence depending on social variation in the community (Labov 2001, 2007). Therefore, a leading view in the sociolinguistic literature is that children are not the group through which change is advanced in the community: this role is instead attributed to adolescents (e.g. Eckert 1997; Kerswill 1996; Kerswill and Williams 2000; Labov 2001; Sankoff 2004; Smith and Holmes-Elliott 2022; Tagliamonte and D’Arcy 2007, 2009). Nonetheless, the changes that are advanced in adolescence often begin in early childhood (Roberts 1999; Smith and Holmes-Elliott 2022; Tagliamonte and D’Arcy 2007): that is, sociophonetic change adopted by adolescents may have its origins in earlier childhood variation.

When children enter school and start socializing outside the family nucleus, their exposure to variation in the community increases at that time through contact to new peers and adult caretakers (Berthele 2002; Díaz-Campos 2005; Foulkes and Docherty 2006; Kerswill 1996; Nardy et al. 2013, 2014; Starks and Bayard 2002; Tagliamonte and Molfenter 2007). Because children are sensitive to external influences, this new stage of socialization may have some influence on their subsequent use of certain variants (Foulkes and Docherty 2006; Nielsen 2014). Nardy et al. (2014) observed that French speaking children in the first year of primary school converged in their use of a non-standard variant. Various studies of the Bavarian variety of German have shown an increased influence of Standard German both in children (Wolfswinkler and Harrington 2021) and in adults (Bukmaier et al. 2014; Kleber 2020; Müller et al. 2011). In Wolfswinkler and Harrington’s (2021) longitudinal study of children over their first three years of primary school, the changes in the direction of the standard were in monophthongal Bavarian vowels that, for reasons to do with coarticulation and undershoot, happened to vary in the direction of the standard. By contrast, there were no changes to Bavarian diphthongs such as /oa/ (in e.g. Stein “stone” /ʃtoa/) whose phonetic variation does not overlap with the Standard /aɪ/ (Stein /ʃtaɪn/). The main conclusion in Wolfswinkler and Harrington (2021) is that this shift in children towards the standard for only some vowels is not socially motivated but instead driven by what Nycz (2016: 77) refers to as an “automatic accommodative [process]” whose origins are likely to be spontaneous imitation that can take place in the absence of social motivations to do so (e.g. Delvaux and Soquet 2007; Harrington et al. 2018; Nielsen 2011) and that has also been proposed as the main factor by which children may drive new dialect formation (Trudgill 2008).

Differences between child and adult speakers in contact situations may also depend on the relative complexity of the features that are changing. For example, while new words can be learned with high proficiency at any time during healthy aging, new phonemes tend to be successfully acquired only during childhood (Chambers 1992; Kerswill 1996; Payne 1980). With respect to dialect contact, Kerswill (1996) provides a comprehensive hierarchy of features ranked according to difficulty of acquisition and age by which they have to be acquired to be mastered. Among the features related to pronunciation, Kerswill (1996: 200) ranks from most difficult to easiest: (1) new phonological oppositions and lexically unpredictable phonological rules; (2) prosodic systems; (3) mergers; (4) Neogrammarian changes (exceptionless shifts […]); (5) lexical diffusion of phonological changes and new phonetic forms of existing morphological categories. Chambers (1992) also puts forward a set of eight principles driving dialect acquisition, of which the following are related to speed of acquisition and feature complexity, and most relevant to our study: (1) simple phonological rules progress faster than complex ones (principle 3, p. 682); (2) acquisition of complex rules and new phonemes splits the population into early acquirers and later acquirers (principle 4, p. 687); (3) eliminating old rules occurs more rapidly than acquiring new ones (principle 7, p. 695); (4) orthographically distinct variants are acquired faster than orthographically obscure ones (principle 8, p. 697).

In summary, dialect contact causes change. This is why Southern Gheg spoken in Tirana is likely to be changing due to contact with Tosk, the standard, and other varieties of Gheg; and perhaps to a certain extent in Bërzhitë because of contact with the standard. Children sometimes regularize changes that adults adopt inconsistently. The first years of schooling, when socialization outside the family increases, may have a strong influence on their use of certain variants. There may also be differences between adult and child speech in contact situations due to relative feature complexity. This is of particular importance because the three dialect features of Southern Gheg analyzed in this paper vary in nature and complexity, as described in the next section.

2.1 Rounding of /a/

Of the three features examined here, rounding of /a/ is the least frequently described in the literature. It is found in a very limited portion of the area where Southern Gheg is spoken, i.e. in and around Tirana, Durrës and Elbasan, and has been reported to be a recent innovation that was still progressing in these communities half a century ago (Çeliku 1965, 1966, 1968, 2020; Gjinari et al. 2007). As shown in examples (1, 2), stressed /a/ is rounded when it is preceded by one of the nasal consonants /m, n/ but unrounded in other contexts, while Tosk & Standard only have an unrounded vowel:

The variant of the low vowel found in Southern Gheg after nasal consonants has been described as rounded (labializim/buzorëzim in Albanian) by Çeliku (1968: 125), who does not use a phonetic symbol to transcribe the resulting variant, but orthographic symbols <o> or <a^o>, with the latter meant to represent a “closed labial /a/”. On the other hand, Gjinari et al. (2007: 7) describe the Southern Gheg variant as retracted (e prapme in Albanian). It thus seems that post-nasal /a/ may not only be rounded, but also raised and/or retracted into [ɔ] or [o]. However, there is no indication in the literature that the contrast between post-nasal /a/ and the back vowel /o/ is (contextually) suspended in Southern Gheg. For these reasons, we will use in this paper the phonetic symbol [ɔ] to refer to the rounded variant of /a/, knowing that Southern Gheg does not include an /ɔ/ phoneme.

2.2 Vowel lengthening

Earlier studies (e.g. Beci 1978; Çabej 1976; Gjinari et al. 2007; Topalli 2007) report phonological length contrasts in Gheg, but not in Tosk & Standard.^[2] In some instances, length is used by Gheg speakers to distinguish between words with same-quality vowels, like in (3). Note that Tosk speakers tend to delete unstressed final schwas like that in plakë below (e.g. Çeliku 1971), which sometimes leads to cases of homophony in Tosk but not in Gheg.

Length is more often used for morphological marking in Southern Gheg. For example, it may be involved in mood marking, as in example (4) opposing imperative and indicative (e.g. Beci 1979, 1995):

Gheg vowels also undergo lengthening in indefinite nouns (as opposed to definite nouns), as seen in examples (5) to (7). The marking of indefiniteness with lengthening is restricted to the following contexts (e.g. Çabej 1976; Çeliku 1971; Demiraj 1996; Topalli 2007): when the stressed vowel is in a syllable closed by a liquid consonant, as in (5); when the vowel is in an open syllable, as in (6); when the schwa marking indefiniteness in Tosk & Standard is dropped in Gheg, as in (7). For an indefinite noun like një dash ‘a ram’, for which there is no final schwa in Tosk & Standard and the vowel is in a syllable closed by a non-liquid coda, lengthening should not happen: /daʃ/.

Note that vowel lengthening is a feature that has been reported to undergo leveling in urban areas already a few decades ago (e.g. Beci 1974, 1978; Shkurtaj 1969).

2.3 Monophthongization

Monophthongization (monoftongizim in Albanian) in Southern Gheg is a phonological phenomenon corresponding to the production of a single vowel in contexts where speakers of Tosk & Standard and other varieties of Gheg would produce either a diphthong (diftong) or a vowel sequence (tog zanoresh)^[3] (e.g. Ajeti 1960, 1978; Beci 1970a, 1970b, 1972; Çabej 1936, 1988; Gjinari 1966, 1968; Topalli 2007; Weigand 1913).

The four vowel sequences that are monophthongal in Southern Gheg are: /ie/, /ye/, /ua/ and /ue/. In all cases, only the first vocalic element is produced, as in examples (8) to (11) (Çeliku 1971), which show that Tosk & Standard and other Gheg varieties have (near-)minimal pairs opposing vowel sequences and monophthongs (second line), while Southern Gheg only has monophthongs (first line):

The distribution of /ie/, /ye/ and /ua/ is lexically-driven, that is, it cannot be predicted from the phonological environment or other linguistic criteria. On the other hand, /ue/ is found in the productive morpheme <ues(e)> that expresses a person’s occupation: pastrues(e) ‘teacher’, mësues(e) ‘cleaner’, etc.

2.4 Aims and hypotheses

The overall aim of this study is to understand how the vocalic system of Southern Gheg is being shaped by contact with Tosk speakers and the Tosk-based standard variety. With contact being more intense in Tirana than in Bërzhitë following a series of socio-political events in Albania, we hypothesize that there could be an urban-rural divide in the stage of the possible changes where urban speakers living in Tirana would be more advanced. Should evidence of these changes be found in the adult population and provided that these are not completed yet, we also hypothesize that first grade children could be at a more advanced stage than adults (Austin et al. 2022; Chambers 1992, principle 4; Cournane 2019; Harrington et al. 2018; Hudson Kam and Newport 2005; Kerswill 1996; O’Shannessy 2019; Roberts 1999; Sanz-Sánchez and Moyna 2022; Smith and Holmes-Elliott 2022; Tagliamonte and D’Arcy 2007; Trudgill 2004; Wolfswinkler and Harrington 2021).

Beyond these general hypotheses, more specific predictions can be formulated regarding the order and rate of change of the three dialect features being investigated. First, the features are of different types: 1) rounding of /a/ is contextual (phonetic); 2) contrastive vowel lengthening is morpho-phonologically conditioned; 3) the opposition between monophthongs and vowel sequences is mainly lexically conditioned. Second, two different mechanisms, i.e. feature loss and acquisition, are involved for Southern Gheg speakers aligning on Tosk & Standard: 1) loss of [ɔ] following nasal consonants, as only [a] is produced across contexts in Tosk & Standard); 2) loss of vowel length contrasts, as there are only short vowels in Tosk & Standard; 3) acquisition of the opposition between monophthongs and vowel sequences of Tosk & Standard, as Southern Gheg only has monophthongs.

If eliminating old rules occurs more rapidly than acquiring new ones (Chambers 1992, principle 7), then the loss of rounding of /a/ and the loss of contrastive length are expected to be at a more advanced stage than the acquisition of the monophthong/vowel sequence opposition. Additionally, if new phonological oppositions and lexically unpredictable phonological rules are the most difficult ones to acquire and progress more slowly (Kerswill 1996; Chambers 1992, principle 3), then the loss of contextual /a/ rounding is expected to be more advanced than the loss of morpho-phonologically conditioned vowel lengthening and the acquisition of the lexically unpredictable opposition between monophthongs and vowel sequences. Table 1 summarizes the relative difficulty with which speakers are expected to adopt these features and consequently, their predicted stage of change.

A secondary aim of this study is to overcome the scarcity of instrumental work carried out on the sound systems of the dialects of Albanian. In addition to the first acoustic description of the three dialect features under investigation, we will present a first sketch of the oral vowel space of Southern Gheg. This vowel space will be used as a baseline with which the three features can be compared and upon which future work may build. To our knowledge, there has been no impressionistic report of differences in the quality of the oral vowels of Southern Gheg versus Tosk & Standard and other varieties of Gheg. Therefore, we do not expect differences between the vowel space of urban and rural speakers that could be attributed to contact. However, we do expect the vowel space of adults and children to differ due to maturational factors (e.g. Chung et al. 2012; Lee et al. 1999; Pettinato et al. 2016).

3.1 Speakers and recordings

Sixty-five (65) speakers participated in this study, including 37 children and 28 adults. The children, 18 girls and 19 boys, were in first grade, aged 6 and 7 years old and had just started to read. The 28 adults, 24 women and 4 men aged 29–73 years old^[4] ( x ‾ = 45 ), were approached via the children’s primary schools; they were either parents, grandparents, teachers or acquaintances recruited through snowball sampling. All participants were born and raised in the location where they were recorded. They and their parents were native Southern Gheg speakers. Twenty (20) children and 14 adults lived in the capital, Tirana, and represent the urban speakers. The rural speakers are 17 children and 14 adults living in Bërzhitë. None of these 14 adults commuted to Tirana for work or reported going to the city frequently.

The recordings took place in quiet rooms of primary schools in Tirana and Bërzhitë. A Beyerdynamic TG H54c head-mounted microphone was used and the speech signal was digitally recorded (44,100 Hz, 16 bits) with a Tascam US-2 × 2 and the Speech Recorder software (Draxler and Jänsch 2004). Fifty-nine (59) out of the 65 participants, including the 37 children and 22 adults, took part in a picture naming task designed for children not yet proficient in reading and writing, as shown in Figure 3, in which a child responds to the image of an animal on a laptop screen. Specifically, their task was to name images of culturally and age relevant objects in their indefinite form (e.g. a/one horse; see Section 2.2), “as they would at home”. If they produced another word than the one expected (e.g. donkey instead of horse), the Albanian-speaking experimenter gave them extra clues to prompt them to produce the expected word. Each image was presented four times, in a random and counterbalanced order.

Figure 3:

Example of set-up and image shown to the participants for the picture naming task. Here, the expected word was kalë ‘horse’. The images used were either under Pixabay license or the property of the second author.

The remaining six adult participants took part in a wider-ranging series of reading tasks,^[5] including reading all words from the picture naming task described above in carrier phrases. Specifically, they were presented on a laptop screen with sentences such as domate thoni domate ‘tomato say tomato’, wherein the target word was domate. They were instructed to read the sentences silently first, then to utter them “as they would in their home local dialect”. Each carrier phrase containing two target words was presented twice (four repetitions of each word), in a random and counterbalanced order. We inspected the data to make sure the same trends were observed independently of the task.

3.2 Materials

The picture-naming task consisted of 38 words of interest, presented in Table A1 in Appendix A, and 16 fillers not considered here. These words were selected from a longer list following an unpublished pilot experiment conducted with a group of 10 Albanian children aged 6–7 years. The reading task featured the same 38 words of interest, plus 203 words intended for other work. The vocalic nucleus of the stressed syllable of each of these words is the target of this study.

The lexical items selected for the experiment had to be depictable in images and meaningful to children aged 6–7 years old. For these reasons, it was not always possible to control for the phonetic environment of the vowels, the stress pattern, and the number of syllables of the words. For instance, the indefinite nouns selected to study vowel lengthening were compared with a set of words not expected to undergo lengthening (Çeliku 1971), but not with minimal pairs (see Appendix A) due to children’s late mastery of the definite/indefinite contrast across languages (e.g. van Hout et al. 2009). Tokens of insufficient quality, masked by an outside noise, or different words altogether were removed (515 tokens or 5.2% data loss). Our corpus therefore consists of 9365 tokens (≈ 38 words × 4 repetitions × 65 speakers).

3.3 Data processing

The speech signal was forced-aligned with the language-independent setting of WebMAUS General (Kisler et al. 2017; Schiel 1999), then structured into a speech database using EMU-SDMS (Winkelmann et al. 2017). Segment boundaries were hand-corrected when needed, with vocalic nuclei identified as zones of high intensity, periodicity on the waveforms, and well-defined formant structure on the spectrograms.

Formant frequencies were estimated with linear prediction coding (LPC) using the Burg algorithm (other parameters were: time step: 0.0 s; maximum number of formants: 5; maximum formant: 7,000 Hz for children, 5,500 Hz for women, 5,000 Hz for men; window length: 0.025 s; pre-emphasis from: 50 Hz). Formants that had been incorrectly tracked (e.g. F3 for F2 in back vowels) were manually corrected. The first two formants (F1 and F2) of each vowel were estimated at 11 equally spaced time points between their acoustic onset and offset. These trajectories were then smoothed with a five-point median filter.

3.4 Vowel space

The vowels from 23 words were analyzed to illustrate the oral vowel space of Southern Gheg: 4 words with /i/, 3 with /y/, 5 with /u/, 3 with /e/, 4 with /o/ and 4 with /a/ (see Appendix A). Note that we did not consider /ə/ because it is usually nasalized in Gheg (Granser and Moosmüller 2002). Short and long vowels were pooled for the purposes of displaying the vowel space because they are not known to differ in quality (Beci 1995). Potential differences in the vowel space of the four groups of speakers were explored by decomposing all time-normalized F1 and F2 trajectories into sets of half-cycle cosine waves (for details on discrete cosine transformation [DCT], see Harrington and Schiel 2017; Watson and Harrington 1999; Zahorian and Jagharghi 1993). The first three DCT coefficients, k ₀, k ₁, and k ₂, that are respectively proportional to the mean, linear slope and curvature of a formant trajectory, were used to calculate the Euclidean distance between two vocalic categories. For instance, the distance between each token of /i/ from speaker s and the mean value of all tokens of /y/ from speaker s in the six-dimension DCT space was calculated using (1), then the same was done for each token of /y/ relative to mean /i/. The resulting values would then allow us to evaluate whether /i/ and /y/ were similarly distinct across the four groups of speakers.

where x → s is a given token produced by speaker s, || denotes vector magnitude and c → is the centroid of the category relative to which the Euclidean distance ed is calculated.

The distance between tokens belonging to pairs of vocalic categories like /i/-/y/ was set as the response variable in linear mixed effects regression models (lme4 and lmerTest packages in the R environment; Bates et al. 2015; Kuznetsova et al. 2017; R Core Team 2021). We started with fully specified models as in (2), then optimized the fit by removing non-significant factors or interactions as determined by the step() function:where response is the response (or dependent) variable, Dialect is a two-level fixed factor (urban, rural), AgeGroup is also a two-level fixed factor (adults, children), Speaker is a random factor for the participants and Word is a random factor for the lexical items. Where relevant, post-hoc multiple pairwise comparisons were computed with the emmeans package (Lenth 2022).

For visualization purposes only, the formant values sampled at the vowel temporal midpoint were speaker-normalized by conversion to z-scores using the procedure based on Lobanov (1971) which has been shown to be an effective technique in reducing the variation between speakers (Adank et al. 2004). However, as pointed out by Brand et al. (2021), z-score transformation can lead to distorted vowel spaces should the number of tokens in a vocalic category be over- or under-represented in some or all speakers. As an alternative technique, dubbed Lobanov 2.0, Brand et al. (2021) suggest an additional step that involves calculating means per vowel category, the result of which is used to compute a grand mean and standard deviation, as in formula (3):

where F s , k ∗ and F s , k are respectively normalized and raw frequency values of a given formant (F1 or F2) produced by speaker s in vowel category k; μ ( μ F s ) is the grand mean of mean values of that formant by speaker s in all vowel categories; σ ( μ F s ) is the standard deviation of mean values of that formant by speaker s in all vowel categories.

3.5 Rounding of /a/

The vowels from three words with [ɔ] were used to investigate this feature (see Appendix A). The distance of all [ɔ] tokens was calculated both to the centroid of /o/ and to the centroid of /a/ (from the oral vowel space in Section 4.1) in a six-dimensional DCT space calculated from F1 and F2. The orthogonal projection op of token x ⃗ s i.e., of an [ɔ] token produced by speaker s was calculated as in (4):

where c ⃗ a and c ⃗ o are the mean values (or centroids) of all /a/ and /o/ vowels produced by speaker s in the DCT space and ⊙ is the scalar (inner) product of two vectors (for more details, see Stevens et al. 2019). The closer to 1 the resulting value, the more similar [ɔ] is to /a/; the closer to −1, the more similar it is to /o/.^[6]

Visual inspection of this data revealed that two groups of speakers had bimodal distributions (see Figure 7) for which linear mixed effect regressions of the kind in (2) are not optimal since they are models for the mean (e.g. Baayen 2008). We therefore applied a mixture discriminant analysis (MDA; Fraley and Raftery 2002) to the four classes of our dataset, i.e. to the four groups of speakers. MDA is a classification algorithm that allows classes to be multimodal by applying Gaussian mixture modeling (GMM; Reynolds 2009) within each class. Using the MclustDA() function with default settings from the mclust R package (Scrucca et al. 2016), GMM automatically established the number of components (i.e. the number of Gaussian distributions) within the four classes of the dataset, as well as their parameters (mean and standard deviation). The results confirmed our observation that the o p ( x → s ) data from two classes, the urban adults and the urban children, had unique Gaussian components, while the data from the other two classes, the rural adults and the rural children, were instead each split into two components.^[7] We thus analyzed rounding of /a/ separately for the unimodal urban and bimodal rural speakers. For the urban speakers, o p ( x → s ) was set as the response variable in a linear mixed-effects regression model as in (5):where Word is a fixed factor instead of a random one as in formula (2) because it has only three levels (see Appendix A; Lee et al. 2006), and the other terms are identical to those in (2).

For the rural speakers, the mean value of the components obtained from the GMM was consistent with the peaks of the bimodal distributions in Figure 7 and comparable across adults and children. Furthermore, these mean values were interpretable in relation to the +1 and −1 values associated with /a/ and /o/ respectively, that is, the GMM split the data into /a/-centered components and /o/-centered components. Given this, applying maximum posterior probability, a factor was created out of the data split (Reynolds 2009), with tokens labeled as belonging to either an /a/-centered component or an /o/-centered component as determined by the GMM. We then set o p ( x → s ) as the response variable in a linear mixed-effects regression model as in (6):where Component is a two-level fixed factor (/a/-component, /o/-component) and the other terms are identical to those in formula (5).

3.6 Vowel lengthening

Vowel duration was measured after hand-correction of the boundaries marking the absolute onset and offset of the vowels (see Section 3.3 for criteria). The statistical analysis was carried out on a log-transformed vowel duration, which helped reduce the skewness of residual distribution. The fully specified regression model initially tested is presented in (7):where log(response) is the log-transformed vowel duration, Length is a two-level fixed factor (long, short) and the other terms are identical to those in formula (2).

3.7 Monophthongization

Monophthongization was assessed by analyzing the time-varying shape of the F1 and F2 trajectories over the course of the vocalic nuclei. We assumed that the dynamic variation in the formants of monophthongal vowels would be less than for vowel sequences (although not necessarily entirely flat, especially due to coarticulation with flanking segments; e.g. Strange and Jenkins 2013). Nuclei of the vowel sequences were also expected to diverge from the monophthongal ones more clearly in their second halves given that the latter miss the second vocalic component (see Section 2.3). Using the mgcv package, generalized additive mixed models (GAMMs; Wood 2011, 2017) were fitted to the time-normalized trajectories of F1 and F2 in the vocalic nuclei of words with /ie/, /ye/, /ue/ and /ua/, which are produced as monophthongs in Southern Gheg, but as vowel sequences in Tosk & Standard (see Appendix A). Additionally, words with monophthongal /i/, /y/ and /u/ which were not expected to differ between urban and rural speakers were included in the models as a baseline.

In order to model interactions, we created a single fixed factor, Dialect_AgeGroup_Vowel, in (8). This composition of all fixed factors, i.e. of the speakers’ origin, the age group, and the vowel was the means by which we allowed them to interact in the models (Sóskuthy 2017):where response is the response variable (F1 or F2), TimePoints are the 11 measurement points of each formant, Dialect_AgeGroup_Vowel is a 28-level factor derived from 2 levels of origin (urban, rural) × 2 age groups (adults, children) × 7 vowels (/ie/, /ye/, /ue/, /ua/, /i/, /y/, /u/), Speaker represents the participants, and Word, the lexical items. Formula (8) includes a smooth term for the TimePoints, a parametric term for Dialect_AgeGroup_Vowel, a smooth term for the interaction between TimePoints and Dialect_AgeGroup_Vowel, as well as random smooths for the interaction between TimePoints and Speaker, and the interaction between TimePoints and Word. This fully specified model was compared with simpler ones using the compareML() function. Differences and similarities between formant trajectories were explored via the various graphs presented in Section 4.4, which were obtained using the tidymv package (Coretta 2021).

4.1 Vowel space

Since empirically-based descriptions of Southern Gheg are scarce at best, the aim of this section is to report acoustic properties of its oral vowel system, as a reference for future work and a control condition for the dialect features investigated in the next sections. We do not expect the vowel quality of urban and rural speakers to differ because there is no indication in the literature that the vowel space of Southern Gheg is different from that of Tosk & Standard and could have been influenced by more contact in Tirana than in Bërzhitë. However, we expect differences between adults and children due to maturational factors, for example a more expanded vowel space in children (e.g. Chung et al. 2012; Lee et al. 1999; Pettinato et al. 2016).

Figure 4 shows the distribution of the six oral vowels of Southern Gheg in F1/F2 planes. The speakers’ vowel space is triangular, with the mid-vowels /e o/ spread between the corner vowels /i u a/. The /y/ vowel is well separated from /i/, and even more so for the urban children, whose /y/ is more back than those of the other speakers. The rural speakers have a more spread /e/ than the urban speakers, with very close vowels that overlap with /i/ and very open ones that overlap with /a/.

Figure 4:

F1 × F2 planes of oral vowels. Measurements taken at vowel midpoint, data normalized with procedure (3).

The inter-Euclidean distance in the six-dimension DCT space between pairs of adjacent vowels calculated with (1) is presented in Figure 5. Pairs that have similar properties or that tend to overlap in the acoustic space have smaller values than pairs that are very distinct (for example, /o/-/u/ are much more similar to each other than /u/-/y/). The predicted inter-Euclidean distance values for the different levels of the significant factors or interactions in the regression models fit to the data are reported in Figure 6.

Figure 5:

Distribution of inter-Euclidean distance between pairs of vowels in 6-dimension DCT space.

Figure 6:

Predicted inter-Euclidean distance between pairs of vowels in 6-dimension DCT space for the different levels of the significant effects or interactions.

For the three pairs of vowels that have a single black line in Figure 6, /e/-/a/, /a/-/o/ and /o/-/u/, only the effect of AgeGroup was significant, with inter-Euclidean distances predicted to be larger for children (/e/-/a/: β = 325, t[56.54] = 6.96, p < 0.001; /a/-/o/: β = 183, t[63.45] = 4.46, p < 0.001; /o/-/u/: β = 109, t[65.23] = 3.65, p < 0.001), which is compatible with our expectations. This is also visible in Figure 5, where the distribution of the child data is more right-aligned. For the /i/-/e/ pair, an AgeGroup difference was also found (β = 208, t[23.75] = 4.45, p < 0.001), in addition to an effect of Dialect. A larger distance is observed for the rural speakers (β = 121, t[20.28] = 2.43, p = 0.024), meaning that their /e/ vowels are further away from /i/ than they are for the urban speakers. This was not expected, but is consistent with the distribution of /e/ that is more spread for rural speakers in Figure 4. A significant interaction between Dialect and AgeGroup was found for the /u/-/y/ (β = 253, t[61] = 2.57, p = 0.012) and /y/-/i/ (β = 426, t[60.92] = 3.48, p < 0.001) pairs. This was not expected either, but it is consistent with the backer /y/ observed in urban children, because the distance between /i/-/y/ is larger for this group than the rural children, while the opposite is found for the /y/-/u/ pair.

The acoustic properties of the vowel system of the different groups of speakers presented so far will be used in the following sections as a control condition to investigate the three dialect features of Southern Gheg that might be undergoing change due to contact with Tosk speakers and the standard.

4.2 Rounding of /a/

In this section, we analyze contextual rounding of /a/, which happens when this vowel is stressed and preceded by one of the nasal consonants /m/ or /n/. Southern Gheg speakers influenced by Tosk & Standard are expected to unround [ɔ] in this context. We thus expect [ɔ] tokens to be more similar to /a/ in urban than rural speakers, and in children than adults. The leftmost and middle panels of Figure 7 illustrate the position of [ɔ] in the speakers’ oral vowel space. In the urban setting (blue), most tokens of [ɔ] overlap with /a/. Those of the urban children tend to be concentrated in the lower part of the acoustic space populated by /a/. In the rural setting, tokens of [ɔ] are spread over /a/ and /o/, with those of the adults concentrated in an area intermediate between /a/ and /o/.

Figure 7:

Left and middle: location of [ɔ] tokens in the F1 × F2 space superimposed on 95% confidence ellipses for other vowels from four groups of speakers. Right: density distributions of orthogonal projection of [ɔ] between /a/ and /o/.

The output of the orthogonal projection of [ɔ] between /a/ and /o/ is presented in the rightmost panels of Figure 7. The four groups of speakers have very different density profiles. The urban children show one high peak centered around +1, which indicates that their [ɔ] tokens are very similar to each other and consistent with /a/. Note that the few tokens that seem to overlap with /o/ in the bottom left plot may have been produced by urban children whose /o/ is raised and acoustically similar to /u/, which would yield orthogonal projection scores for [ɔ] that are not necessarily close to +1. There is a tendency for [ɔ] in urban adults to be centered near +1, but with a more spread distribution, suggesting more variation than for the urban children. The rural adults’ tokens peak at around +1 and −1, which indicates the presence of both /o/-like rounded and /a/-like unrounded variants. A fairly large number of intermediate pronunciations are spread in between the two peaks. The rural children also display the two-peak pattern, but the −1 peak is much lower than the peak centered around +1.

As explained in Section 3.5, these differences in data distributions make the rural and urban speakers distinct enough that fitting separate statistical models was advisable. For the urban speakers, a significant effect of AgeGroup was found (β = 0.61, t[31] = 4.15, p < 0.001), with a higher estimated mean for the children, indicating that they have produced [ɔ] tokens that were closer to /a/ than the adults. Similarly, a significant effect of AgeGroup was found for the rural speakers (β = 0.69, t[29.83] = 3.62, p = 0.001). The [ɔ] tokens produced by the rural children were closer to /a/ than those of the rural adults irrespective of whether they belonged to the /o/-component or the /a/-component. The change towards /a/ is thus more advanced in Tirana than in Bërzhitë, with tokens grouped into one Gaussian component in the former, but two Gaussian components in the latter. It is also more advanced, i.e. with values closer to /a/, in children than in adults in both locations.

4.3 Vowel lengthening

In this section, we investigate the phonological vowel length contrast which is used for instance in marking (in)definiteness. Gheg speakers influenced by Tosk & Standard are expected to produce short vowels only. As for the previous feature (rounding of /a/), we expect the contrast to be maintained more in the rural than in the urban setting, and more by adults than by children. From Figure 8, it is clear that all groups of speakers distinguish between long and short vowels. The children produced vowels with greater durations than adults, possibly due to a slower speech rate (Martins et al. 2007). Figure 8 also suggests greater durations for rural than urban speakers.

Figure 8:

Duration of vowels in short and long words.

The statistical analysis shows a significant three-way interaction between Dialect, AgeGroup and Length (β = 0.14, t[61.28] = 2.08, p = 0.040). The predicted duration for each level of this interaction is shown in Figure 9. The results of the post-hoc test indicate that the rural children (solid red) produced long vowels with significantly larger durations than those of the long vowels from urban adults (dashed blue) (t[72.4] = 4.28, p = 0.001). No other group differs from one another with respect to long vowels. For the short vowels, none of the groups are significantly distinct from each other either. However, the short vowels of the children (left dots, solid lines) are not significantly different from the long vowels of the adults (right dots, dashed lines), which, taken together with the very large durations of long vowels in rural children, confirm the slower speech rate of children. Within each group, short and long vowels are systematically significantly different (urban adults: t[30.5] = 4.69, p = 0.001; rural adults: t[30.1] = 4.57, p = 0.002; urban children: t[30.5] = 4.5, p = 0.002; rural children: t[32.4] = 6.78, p < 0.001), indicating contrastive length has been preserved by all groups. Beyond the three-way interaction, only the main effects of Length and AgeGroup are significant (Length: β = 0.42, t[30.4] = 4.69, p < 0.001; AgeGroup: β = 0.24, t[80.2] = 2.93, p = 0.004).

Figure 9:

Predicted duration of short and long vowels across speaker groups (exponentiated).

4.4 Monophthongization

In this section, we analyze a set of words where Southern Gheg has monophthongs /i, y, u/, whereas Tosk & Standard have vowel sequences /ie, ye, ue, ua/. Southern Gheg speakers influenced by Tosk & Standard are expected to produce vowel sequences in these words. As before, we expect monophthongization to be maintained more in the rural setting than in the urban one, and more by adults than by children. A significant effect of Dialect_AgeGroup_Vowel was found for F1 and F2 trajectories (F1: F[27] = 107, p < 0.001; F2: F[27] = 438, p < 0.001). The first and third rows of Figures 10, 11, 12, and 13 show predicted smooths for F1 and F2 respectively. The gray smooths correspond to formant trajectories of monophthongal /i/, /y/ or /u/ from the vowel space (Section 4.1), presented here to better illustrate the amount of spectral change found in the vowel sequences, but not further analyzed. The blue smooths correspond to formant trajectories of the vocalic nuclei under investigation, for urban and rural speakers separately. The difference between these smooths, presented in the second and fourth rows, shows how and when formant trajectories differ between urban and rural speakers over the course of the vocalic nuclei, with portions of the pairs of trajectories that are significantly different (p ≤ 0.05) displayed in solid green, and portions that are not significantly different (p > 0.05) in dotted red. Adults and children are presented separately, with the latter expected to have higher formant values.

Figure 10:

Predicted formant trajectories over time-normalized /ie/ and /i/ in the first (F1) and third (F2) panel rows. Second and fourth rows show differences in smooths of /ie/ only.

Figure 11:

Predicted formant trajectories over time-normalized /ye/ and /y/ in the first (F1) and third (F2) panel rows. Second and fourth rows show differences in smooths of /ye/ only.

Figure 12:

Predicted formant trajectories over time-normalized /ue/ and /u/ in the first (F1) and third (F2) panel rows. Second and fourth rows show differences in smooths of /ue/ only.

Figure 13:

Predicted formant trajectories over time-normalized /ua/ and /u/ in the first (F1) and third (F2) panel rows. Second and fourth rows show differences in smooths of /ua/ only.

In Figure 10, we observe a clear distinction between monophthongal /i/, which has quite stable formant values over its entire course, and the vowel sequence /ie/, where F1 rises and F2 drops in the second half, consistently with /i/ changing into /e/. As shown in row 2 of Figure 10, F1 of /ie/ is not significantly different between urban and rural speakers. In the fourth row, the F2 drop is significantly greater in the urban setting, with similar sequence onsets but different offsets for adults, and both different onsets and offsets for children. This suggests /ie/ was produced as a vowel sequence by urban and rural speakers alike.

In Figure 11, /y/ and /ye/ have distinct formant trajectories for the urban speakers, but less so for the rural speakers. The former have a rising F1 in the second half of the sequences, as expected with /y/ changing into /e/, when F1 becomes significantly distinct from that of the rural speakers (second row). There is also evidence of a greater F2 drop in /ye/ for urban speakers. Indeed, in row 4 of Figure 11, urban adults’ F2 starts high, then drops until it reaches rural adults’ F2. The two groups of children have similar F2 frequencies at the onset, after which that of the urban children drops until it is significantly lower. This suggests /ye/ was produced as a vowel sequence by urban speakers, but as a monophthong by rural speakers.

In rows 1 and 3 of Figure 12, /u/ and /ue/ are generally more distinguished by urban than rural speakers. Urban children produced /ue/ with a rising F1, such that the second half is significantly distinct from that of the rural children (row 2) and consistent with high /u/ changing into mid-high /e/. The adults remain similar over the entire course of the nuclei for F1, but the urban participants have a significantly higher (and further rising) F2 than the rural ones (row 4). This trend, also observed to an even greater extent among children, reflects the transition from the back vowel /u/ to the front vowel /e/. Once more, this suggests /ue/ has been produced as a vowel sequence by urban speakers, but as a monophthong by rural speakers.

Finally, in Figure 13, we observe that /u/ and /ua/ are more distinct for urban than rural speakers. Both adults and children from the urban setting have produced vowels with rising F1 and F2 (rows 1 and 3), which is consistent with high back /u/ changing into low central /a/. Rows 2 and 4 of Figure 13 show that the vowels of the urban and rural speakers are similar at the onset, after which they become significantly different as formant frequencies rise for urban speakers. This indicates the latter have produced /ua/ as a vowel sequence, but rural speakers have uttered monophthongs.