1. Introduction

T-glottaling is the realization of the voiceless alveolar plosive /t/ as a glottal stop [ʔ]. It is also sometimes referred to as ‘glottal replacement’ (Milroy, Milroy, Hartley, & Walshaw, 1994), to distinguish it from ‘glottal reinforcement’ (Higginbottom, 1964), that is from the addition of [ʔ] to an oral stop. T-glottaling has been attested in numerous accents of English spoken both in Britain (Fabricius, 2002; Milroy et al., 1994; Trudgill, 1974) and in the United States (Eddington & Channer, 2010; Eddington & Taylor, 2009; Huffman, 2005; Levon, 2006; Roberts, 2006; Seyfarth & Garellek, 2015). Recently, it has been shown that in American English, [ʔ] is one of the possible realizations of /t/ across word boundaries before vowels, that is in contexts such as righ[ʔ] in. Such realizations are attested by Levon (2006), in the speech of New York Reform Jews, by Roberts (2006), for rural Vermonters, and by Eddington and Taylor (2009), who compared Utahns to speakers from elsewhere in the U.S. Eddington and Channer (2010) argued that the likelihood of t-glottaling is linked to the frequency with which a given /t/-final word occurs before consonant-initial words. This is an intriguing proposition. The evidence that the original paper provides for this proposition, however, can be further strengthened. Using different data, an improved statistical analysis, and a more precise method of calculating the proportion of occurrence of a word in preconsonantal contexts, the present paper confirms the finding of Eddington and Channer (2010): Higher rates of occurrence in preconsonantal contexts correlate with a higher chance of glottaling in both preconsonantal and prevocalic contexts. Consequently, the case that the original paper made for storage of phonetically detailed representations is strengthened. The contribution of the present study is twofold. First, it investigates the influence of linguistic and social factors on the likelihood of prevocalic t-glottaling across word boundaries in Columbus, Ohio. Second, it bolsters the support given by Eddington and Channer (2010) to models of phonology that incorporate phonetically rich representations by providing further evidence that t-glottaling shows a ‘contextual frequency’ effect (Forrest, 2017).

1.1. Glottaling

The glottal stop [ʔ], in its canonical realization, is a plosive produced by drawing together, and then releasing, the vocal folds. Glottal stops, however, show considerable variability in their realization, ranging from full stops to laryngealized phonation (Garellek, 2013, p. 2). In fact, the most common realization of [ʔ] in English is that of “irregular spacing of pitch periods in the waveform” (Dilley, Shattuck-Hufnagel, & Ostendorf, 1996, p. 428; Pierrehumbert & Talkin, 1992), a type of ‘incomplete [ʔ]’ identified by Garellek’s (2013, p. 33–54) articulatory study. Consequently, in this study I use the term ‘glottal stop’ in this broader sense, including both full stops and laryngealized phonation. An example of t-glottaling—intervocalic /t/ at a word boundary realized as a form of [ʔ]—is presented in Figure 1. It shows a case of t-glottaling from the Buckeye corpus (Pitt et al., 2007), the source of data for the present study. The waveform and spectrogram show /t/ realized as [ʔ] by a young woman from Columbus, Ohio. A token of [ʔ] is visible as irregular pitch periods. Though the [ʔ] is ‘incomplete,’ such realizations do give the auditory impression of a glottal stop, not unlike that well-attested for British English accents (Fabricius, personal communication). As for possible sources of this auditory impression, dips in f0 and amplitude have been identified as cues that listeners rely on for detecting glottal stops even when there is no cessation of voicing (Hillenbrand & Houde, 1996).

Figure 1

Waveform and spectrogram of get everywhere produced by a young woman from Columbus, Ohio (Source: the Buckeye corpus, Pitt et al., 2007). The portion of the signal associated with the percept of [ʔ] shows irregular pitch periods.

T-glottaling has long been observed to take place in American English dialects in several phonological environments (see Table 1 for a brief overview of reports). As is the case with other consonantal features, t-glottaling has been described more extensively for British English than for American English accents (though the number of studies on variation in consonants in American English accents is growing, e.g., Zhao, 2010; Gylfadottir, 2015; Yuan & Liberman, 2011). T-glottaling has been reported to occur word-internally (e.g., Trager, 1942; Zue & Laferriere, 1979), across word boundaries before sonorant consonants (Pierrehumbert, 1994) and before plosives (Huffman, 2005). It has only relatively recently been reported to occur across word boundaries before vowels, as in righ[ʔ] ankle (Eddington & Taylor, 2009; Levon, 2006; Roberts, 2006).

Table 1

Overview of contexts for which t-glottaling has been reported to take place in American English.

Context	Examples	Sources
before nasals	portent	Trager (1942), Zue and Laferriere (1979)
before sonorants	hat rack, about you	Pierrehumbert (1994), Kaźmierski et al. (2016)
before plosives	beet counter	Huffman (2005), Dilley and Pitt (2007)
syllable-finally	cat, sent, belt	Selkirk (1972), Kahn (1976), Cohn (1993)
IP-finally	‘hat, daypack’	Huffman (2005)
across words, before vowels	right around	Levon (2006), Eddington and Taylor (2009)

As Eddington and Channer (2010) observe, prevocalic glottaling across words may be seen as surprising in American English, when one considers the rarity of prevocalic glottaling word-internally. Within words, prevocalic glottaling is generally less commonly reported for North American than for British varieties: In contexts such as ci[ɾ]y, flapping typically takes place, precluding glottaling. Although word-internal prevocalic t-glottaling is attested in American dialects—it has been observed before /ən/ in dialects which ‘unpack’ syllabic /n̩/, as in moun[ʔə]n (Eddington & Savage, 2012), and also, to a limited extent, in morphologically complex words such as pu[ʔ]ing, wai[ʔ]ing (Patterson & Connine, 2001)—word-internal prevocalic flapping is more common than word-internal prevocalic glottaling. In general, glottaling and flapping tend to occur in mutually exclusive contexts, as elaborated in the following. Some environments allow only glottaling. This is the case word-internally before obstruents and nasals, as in ou[ʔ]put or po[ʔ]ent, as well as in absolute final position, e.g., in tha[ʔ]. Before vowels and syllabic liquids within words, on the other hand, flapping is typical, as in ci[ɾ]y, be[ɾ]er, and li[ɾ]le.¹ Across word boundaries, only glottaling is allowed before obstruents, as in se[ʔ] back, and before sonorant consonants, as in abou[ʔ] you. But across word boundaries before vowels—the environment investigated in the present paper—both flapping and glottaling are common: righ[ɾ] around ~ righ[ʔ] around (Eddington & Channer, 2010).

Eddington and Channer (2010) present a large-scale study of the competition of glottaling and flapping in unscripted speech. They analyzed a sub-part of the Santa Barbara Corpus of Spoken American English (DuBois et al., 2005), looking at all cases of word-final /t/ (preceded by a vowel, nasal, or liquid) followed by vowel-initial words (N = 1,101). In that study, all instances of /t/ were impressionistically coded as [t], [ɾ], [ʔ], or as deleted. The middle column of Table 2 shows the results. In a non-negligible number of cases (262 out of 1,101, that is just under 24%), prevocalic /t/ was realized as a glottal stop. This might be seen as surprising, given the prevalence of prevocalic flapping word-internally in this segmental context.

Table 2

The realization of word final /t/ in Eddington and Channer (2010), based on a sub-part of the Santa Barbara Corpus and in the present data set, based on the Buckeye corpus.

Realization of /t/	Eddington and Channer (2010)	Buckeye
Flapping [ɾ]	656 (59.6%)	4,669 (63.8%)
Glottaling [ʔ]	262 (23.8%)	1,134 (15.5%)
No process [t]	110 (10%)	763 (10.4%)
Deletion [Ø]	73 (6.6%)	751 (10.3%)

2. Data

All bigrams—sequences of one word (w₁) immediately followed by another word (w₂)—in which w₁ ends in /-Vt/ and w₂ begins with a vowel were retrieved from the Buckeye corpus (Pitt et al., 2007) with the help of LaBB-CAT (Fromont & Hay, 2012), a publicly available corpus annotation and management suite. The Buckeye corpus is very well-suited to this study as it contains unscripted speech of a homogenous group of speakers of a variety for which t-glottaling has not been the center of attention. It also comes with hand-corrected phone-level annotations, including allophones of /t/ (an evaluation of the accuracy of the transcriptions is described below). Cases where w₂ was not a lexical item, that is when it was represented as one of the following orthographic forms: um, uh, oh, o, uh-huh were excluded from analyses. This resulted in 7,317 hits, occurring in 5,375 speaker turns. The Buckeye Speech Corpus² contains recordings of 40 speakers from Columbus, Ohio. The participants were recorded conversing with an interviewer on everyday topics. The recordings are altogether around 40 hours long and contain approximately 300,000 word tokens. The city of Columbus, according to the dialectal division of North America of Labov, Ash, and Boberg (2006) belongs to the Midland dialect region. Speech patterns in this region can be expected to be typical of North American English, to the extent that any particular area can. “Many features of the Midland are the default features—that is, the linguistic landscape remaining when marked local dialect features are eroded” (Labov et al., 2006). While there are vocalic features that nonetheless separate the Midland dialect from the neighboring Northern and Southern dialect areas (such as the ‘close approximation’ of the vowels in the LOT and THOUGHT lexical sets, Labov et al., 2006, p. 264), consonantal features, or glottaling specifically, is not known to show any particular pattern in this dialect. The speakers in the corpus are stratified for age (two categories: under 30 and over 40, actual ages are not provided to the corpus user) and gender.

The corpus comes with, among others, a phone-level annotation layer produced by “a group of trained phonetic annotators […] paid for corpus preparation” (Dilley & Pitt, 2007, p. 2341), based on spectrograms, waveforms, and auditory cues. The phone-level annotation layer includes /t/ allophony. A segment was labeled by the corpus annotators as [ʔ] if it “had perceptually creaky voicing accompanied by irregularity in pitch period timing in the waveform” (Dilley & Pitt, 2007, p. 2342). This study relies on these transcriptions, as there are good reasons to believe that the accuracy of coding of /t/ allophony in Buckeye’s segment annotations is high. First, in a published study (Pitt, Johnson, Hume, Kiesling, & Raymond, 2005), the intertranscriber reliability for stops was found to be 93%. Second, in a study investigating sequences of word-final /t/ followed by /j/-initial words (/V_#j/) e.g., about you (Kaźmierski, Wojtkowiak, & Baumann, 2016), 65% of /t/s (528/808) were hand-coded as glottaled, based on a drop in amplitude and lack of release burst. A look at the transcriptions provided with the corpus reveals that 54% (446/833) are labeled as [ʔ] (the slight difference in N seems to stem from differences in querying with the bundled software versus with LaBB-CAT). Finally, in a study even more directly related to the present one, Seyfarth and Garellek (2015) inspected a subset of coda /t/s in the Buckeye corpus and found that the vast majority of [ʔ] labels (1,762/1,824, or 97%) correspond to glottal replacement, with no format transitions or release bursts indicative of [t]. The remaining small minority of cases (62/1,824, or 3%) correspond to glottal reinforcement [ʔ͜t], as they have a detectable release burst.³ Taken together, these results suggest that the accuracy of transcriptions of [ʔ] is high, and that, if anything, [ʔ] could be under- rather than over-reported in Buckeye’s annotations.

All cases where no consonantal sound was present as a transcription of the final sound of w₁ were treated as cases of deletion. Cases where the final consonant of w₁ was transcribed as a voiced plosive /d/ were included together with those transcribed as a flap. The remaining cases, where the final sound of w₁ was transcribed with a plain ‘t’ symbol were coded as [t], denoting a voiceless alveolar plosive. The rightmost column of Table 2 shows a general overview of the initial data set (N = 7,317). A comparison of proportions of the realizations of /t/ in Eddington and Channer’s (2010) results and in the present data set shows a lower glottaling rate, and higher flapping and deletion rates for Buckeye.

For further analysis, including regression modeling, only cases where /t/ was realized either as a flap or glottal stop were kept (N = 5,803). This is motivated by the observation that it is these two allophones of /t/ that compete directly in word-final prevocalic position (cf. Eddington & Channer, 2010, p. 344). The realization of /t/ as [ɾ] or [ʔ] was therefore included as a binary response variable GLOTTALED, with [ɾ] as a reference level, so that effectively the likelihood of glottaling over flapping is modeled.

Figure 2 shows the final data set broken down by gender and age. A glance at the raw data shows that there are no speakers categorically preferring only one realization—everyone shows variability between the two. Further, younger female speakers seem to have higher rates of glottaling than the three other groups, with eight out of ten younger female speakers having a glottaling rate above the mean. Going against this general trend, Speaker 19, an older male speaker, has the highest glottaling rate in the data set.

Figure 2

Individual variation broken down by age and gender. Each bar represents the percentage of glottaling for a single speaker after only glottaling and flapping were left in the data set (N = 5,803). The dashed line represents the mean glottaling rate for all speakers (x̅ = 0.204). Speaker indices are shown on the horizontal axis.

3. Analysis

In any corpus of unscripted speech, the data is, by definition, not controlled at the collection stage, allowing confounding variables to be present. Thanks to multiple regression modeling, where several predictors are included in the same model, confounds can be dealt with at the stage of statistical analysis (Baayen, 2008). In other words, the influence of a variable of theoretical interest can be estimated while the influence of the other predictors is kept constant. In the present analysis, control variables were added mostly through automatic annotation functionality of LaBB-CAT (Fromont & Hay, 2012), as well as data transformation functions of the R package DPLYR (Wickham, Francois, Henry, & Müller, 2017), as discussed separately for each variable below. A mixed-effects logistic regression model was then fitted to the data with the glmer() function from the LME4 package (Bates, Mächler, Bolker, & Walker, 2015) in R (R Core Team, 2018). The inclusion of random effects, that is the use of mixed-effects modeling, is appropriate where several data points are grouped, as is the case here, and is known to prevent the inflation of the rate of Type I errors (Baayen, Davidson, & Bates, 2008). Accordingly, to account for within-speaker variation that is not due to any of the variables listed in Sections 3.1–3.2 below, and to account for the lack of independence of multiple data points coming from the same speaker, SPEAKER was included as a by-subject random intercept term. This takes care of differences in rates of glottaling across speakers. Furthermore, as the influence of the test variable CONSONANTAL PROPORTION (see Section 3.1 below for details on the test variable) might be different on different speakers, a by-subject random slope for CONSONANTAL PROPORTION was included (while including by-subject random slopes for all fixed terms would be desirable, it resulted in singular fit and was therefore not feasible). Similarly, word-level idiosyncrasies could not be ruled out, and were accounted for by including WORD, that is each /t/-final test word, as a by-item random intercept term. Bringing the influence of confounds under statistical control by including them as fixed effects, as well as accounting for interdependencies between data points by including random intercepts, is seen as a considerable improvement compared to the ANOVA test of the study by Eddington and Channer. Finally, as a remedy to initial model convergence issues, the BOBYQA optimizer was used.

3.1. Test variable: Consonantal proportion

The main variable of interest for this study is the proportion of consonant-initial words following a given word in a corpus. The motivation for investigating its influence on t-glottaling comes from Eddington and Channer (2010), yet the details of how it was computed have been slightly improved compared to that study. Here, it was computed based on the SUBTLEX-US (Brysbaert & New, 2009) corpus, a large collection of movie and TV series subtitles, shown to yield frequency measures that give accurate predictions in reaction time experiments. With the help of AntConc, a freeware corpus analysis toolkit (Anthony, 2014), all bigrams where the test word appeared as the first word were retrieved. Next, all words were supplied with a phonological transcription from the CMU pronouncing dictionary with the LOGIOS Lexicon Tool.⁴ Finally, for each test word the number of consonant-initial following words was divided by the sum of consonant-initial following words and vowel-initial following words. By using phonological transcriptions instead of orthography, which was the case in the original study, higher accuracy has been achieved, as words with a consonant letter in spelling where none is present in pronunciation (e.g., hour) and words with no consonant letter in spelling where there actually is one in pronunciation (e.g., use) were classified correctly.

As is illustrated in Figure 3, the /t/-final words analyzed in the present paper tend to be followed by consonant-initial words more often than by vowel-initial words, based on SUBTLEX-US: The mass of the density in both panels is above 0.5. This is the case both in terms of token counts (mean = 0.672, median = 0.669, SD = 0.142, n = 5,803) and in terms of type counts (mean = 0.685, median = 0.696, SD = 0.111, n = 209). Incidentally, the assumption that glottaling is favored before consonants is bolstered by the statistics of glottaling of post-vocalic word-final /t/ across word boundaries in the Buckeye corpus. When the following word starts with a consonant, 5,112 out of 14,124 cases (36%) are glottaled, and when the following word starts with a vowel, only 870 out of 7,323 cases (12%) are glottaled.

Figure 3

Densities of CONSONANTAL PROPORTION values for words in the analyzed data set (Buckeye, N = 5,803), for tokens (left) and types (right). CONSONANTAL PROPORTION values from SUBTLEX-US.

This continuous variable was centered (by subtracting the mean) and standardized (by dividing by one standard deviation), giving CONSONANTAL PROPORTION present in the tables and charts to follow. The hypothesis with regard to this variable, and so the main hypothesis of the study, is that it will be positively associated with the likelihood of a test word undergoing t-glottaling.

4. Results

The results of regression modeling are summarized in Table 3. As is standard practice, p-values for this model were calculated based on asymptotic Wald tests. Each coefficient represents the estimated change in log-odds of glottaling over flapping as the value of the predictor increases (for continuous predictors) or when the level changes from reference to the one indicated in brackets (for categorical predictors). For AGE [YOUNGER], the coefficient obtains when GENDER is held at FEMALE, and for GENDER [MALE] the coefficient obtains when AGE is held at OLDER As the table shows, the contribution of a number of predictors has turned out to be statistically significant. Partial-effect plots of all statistically significant terms are presented in Figure 4.

Figure 4

Effect plots for statistically significant model terms. For each term, the effect of the remaining terms was averaged using the EFFECTS package (Fox & Hong, 2009). The y axis in each plot represents the probability of glottaling. Continuous predictors (Panels A, B, and C) include jittered raw data points, included at the bottom (no glottaling) and top (glottaling). To allow the meaningful placement of the raw data points, the full range of probabilities is used in these panels. For categorical predictors (Panels D, E, and F), a selected portion of the probability scale (0–0.4) is plotted for greater readability.

4.2. Other linguistic variables

The frontness of the initial vowel of the following word has not turned out to be statistically significant (β = –0.01, p = 0.866), thus supporting neither Eddington and Taylor (2009) nor Ostalski (2013). There are two possible interpretations of this negative result: Either the influence of the frontness of the following vowel is indeed negligible, with previous findings perhaps showing experimental artifacts, or the effect is too small to be detected with the present data set. As vowel quality is not of primary concern here, it will not be pursued further.

With regard to the influence of lexical stress (Panel D in Figure 4) t-glottaling has turned out to be more likely if the following syllable is unstressed (β = 0.4, p < 0.001). This result is opposite to that of Eddington and Channer (2010). One possible reason for this differing result, suggested by an anonymous reviewer, is the difference in how stress was coded. Eddington and Channer (2010) took phrasal prominence into account when coding stress: For example, function words which “were given an emphatic rendering by the participant” (Eddington & Channer, 2010: 342) were coded as stressed. In contrast, in the present study only lexical stress was considered, and all function words were coded as unstressed (except all, each, and own). Perhaps more importantly, however, an after the fact investigation revealed that the effect of stress in the present study might be confounded by cases where the initial syllable of the following word was realized as a syllabic nasal: 328 such realizations were discovered in the data set. For instance, while the /t/ at the end of not in a bigram in the data set, not intentional, is intervocalic as far CMU dictionary transcription used for retrieving tokens is concerned, its actual realization in the corpus was [nɑʔn̩tɛnʃnʌl]. Syllabic /n/ is known to favor glottaling (Zue & Laferriere, 1979). Indeed, in the present data set, the glottaling rate in cases where the initial segment of w₂ is transcribed as a nasal is 94% (307/328). By comparison, the glottaling rate in the remaining subset of the data is only 15% (827/5,475). In a model fit to the subset of the data set after removing the 328 cases where [ʔ] was not actually intervocalic, the effect of W₂ STRESS went in the same direction as in the original model, but was not significant (β = 0.17, p = 0.14). In the full data set, the glottaling rate is slightly higher before unstressed syllables (936/4,702; 20%) than before stressed syllables (198/1,101; 18%). In the data set with the following nasals removed, however, the glottaling rate is slightly lower before unstressed syllables (646/4,394; 15%) than before stressed syllables (181/1,081; 17%). Note that remaining effects are not substantially different compared to the original model: Crucially, the effect of CONSONANTAL PROPORTION remains almost unchanged at β = 0.41 (p = 0.003). The extent to which syllabic nasals figure into the analysis of the Santa Barbara Corpus in Eddington and Channer (2010) is unknown. At any rate, the effect of following stressed syllables favoring glottaling found by Eddington and Channer, who took phrasal prominence into account when coding for stress, might support the influence of the presence of prosodic boundaries on glottaling. The effect of stress found in the present paper, due to the unfortunate inclusion of a confound, must remain inconclusive.

The corpus frequency of the bigram (BIGRAM FREQUENCY) and SPEECH RATE DEVIATION, had an effect agreeing with that suggested by previous literature. Both are negatively correlated with the likelihood of glottaling: BIGRAM FREQUENCY (β = –0.13, p < 0.001) and SPEECH RATE DEVIATION (β = –0.12, p = 0.005). Seemingly, then, both an increase in speech rate and high bigram frequency are conducive to w₁ and w₂ being chunked together during motor planning, in which case a more word-like behavior of the two-word sequence transpires. In the present case, this means a higher chance of flapping, the process expected word-internally, to occur (cf. Kilbourn-Ceron, Clayards, & Wagner, 2020).

Given the effect of speech rate, as suggested by an anonymous reviewer, one may wonder whether the lower incidence of glottaling in the Buckeye corpus (15.5%) compared to the Santa Barbara corpus (23.8%) may be due to different overall speech rates in the two corpora. To investigate this possibility, I calculated articulation rate, that is the number of syllables per second of phonation, for all speakers in the two corpora. The extraction was automated with the SYLLABLE_NUCLEI script (de Jong & Wempe, 2009), which detects syllable nuclei, discards pauses, and calculates speech rate metrics. The speakers in the Buckeye corpus tend to speak faster (mean = 4.21, median = 4.16, SD = 0.425, unit: syllables per second) than speakers in the Santa Barbara Corpus (mean = 3.53, median = 3.81, SD = 1.02). As faster speech rate disfavors glottaling, the faster speech rate in the Buckeye corpus may be linked to lower glottaling rates. However, the difference in articulation rates as measured by the SYLLABLE_NUCLEI script might be exaggerated. While the exact same methodology was applied to both corpora, the script reported suspiciously low articulation rates for some speakers in the Santa Barbara corpus (all speaker means are presented in Figure 5). When extreme observations (absolute difference from the mean greater than two standard deviations) are removed, however, the difference in articulation rate between the two corpora persists (Buckeye: mean = 4.19, SD = 0.4, Santa Barbara: mean = 2.72, SD = 0.78).

Figure 5

Articulation rates (number of syllables per second of phonation) in the Buckeye Corpus compared to the Santa Barbara corpus extracted with the SYLLABLE_NUCLEI script. Dots are means of individual speakers.

5. Discussion and conclusion

With regard to the first goal of the paper, the replication of the contextual frequency effect, it has been confirmed that /t/-final words that typically occur before consonant-initial words undergo glottaling at higher rates than words that occur before consonant-initial words less often. As to the second goal of the paper, the quantification of the factors influencing rates of prevocalic t-glottaling across word boundaries in Midland American English, bigram frequency and speech rate deviation have been found to be negatively correlated with t-glottaling, while the results concerning the frontness of the initial vowel of the following word, and the presence or lack of stress on the initial syllable of the following word remain inconclusive. Finally, an effect of an age by gender interaction was discovered. The implications of the effect of social variables will first be discussed, before turning to the wider implications of the replication of the finding of the contextual frequency effect for models of phonological representations.

In the present study, younger age is positively correlated with t-glottaling for female speakers. While the effect of gender for older speakers is not significant, in light of the interaction that gender enters into with age, it (gender) is by no means an irrelevant variable. While for older speakers there is hardly any difference in rates of intervocalic word-boundary t-glottaling between women and men, for younger speakers there is an appreciable difference between the genders. In Columbus, young women are the group glottaling the most. Variants favored by young women have been repeatedly shown to be the variants on the rise in cases of linguistic change in progress (Eckert, 1988; Labov, 1994). To the extent that this pattern is attested in the present data set, it provides an indication that t-glottaling might be undergoing a change in progress in Columbus. This apparent-time indication, however, would of course have to be supplemented with real-time data to ascertain whether or not t-glottaling is on the rise. The case for a change in progress is strengthened by recurring indications of higher rates of t-glottaling among younger speakers in other parts of the country. Eddington and Taylor’s (2009) participants came from Western states (N = 42) (the majority of these from Utah) and from non-Western states (N = 16, including the North, the South, the Midland, and the Mid-Atlantic), and showed the same kind of gender by age interaction as found here, with younger women having highest rates of t-glottaling. The Santa Barbara Corpus speakers selected by Eddington and Channer (2010) also came from both Western (N = 19) and non-Western (N = 21) states, and also showed a facilitative effect of age, though not interacting with gender. Studies focusing on particular speech communities provide an interesting comparison. For New York Reform Jews, t-glottaling seems to be tied to their highly specific ‘mosaic identity and style’ (Levon, 2006), where being affiliated with multiple social groups may exert conflicting pressures: The secular setting of an interview, as well as secular topics, seem to favor glottaling over audible alveolar release for the two young speakers, in contrast to the religious setting of the Youth Group, and religious topics, which disfavor glottaling. Another particularly interesting case is Vermont (Roberts, 2006), where t-glottaling used to be a stigmatized feature, and yet is now spreading among young speakers who otherwise move away from local speech patterns and accommodate to supralocal norms, leading Roberts to posit that the ‘new’ glottaling might be a new and different feature altogether. All these findings taken together potentially point to a ‘nationwide change in progress,’ which Midland American English is participating in. Several such supralocal changes have been posited in the vocalic domain. One of them is the low-back (or LOT-THOUGHT) merger, which, at least in the Midland and the South “is not spreading from any particular point(s) of origin but is appearing roughly simultaneously in several states” (Johnson, 2010, p. 10). Similarly, the Elsewhere Shift (sometimes seen as related to the LOT-THOUGHT merger, cf. Becker, 2019), with the retraction/lowering of the vowels of KIT and DRESS, and with the nasal system of TRAP, recently documented in Lansing, MI (Nesbitt, 2018), was previously found in places so distant from one another as California (Hagiwara, 1997) and Canada (Boberg, 2005).

The hypothesis put forward by Eddington and Channer (2010) has stood its ground when confronted with a different data set and with an improved statistical analysis. The finding that the frequency with which a /t/-final word is followed by consonant-initial words increases the likelihood of that word to undergo glottaling even before vowel-initial words has therefore gained further support. This result contributes to the growing body of evidence that detailed phonetic information is stored in the mental lexicon. The frequency of occurrence in preconsonantal environment can raise the likelihood of glottaling in prevocalic position only if a representation with a glottal stop is stored, as it is not derived online prevocalically. One solution proposed to account for such effects is offered by exemplar models of phonology (e.g., Johnson, 1997; Bybee, 2001). These assume that a phonetically detailed trace of each perceived token is stored.

However, doing away with abstract representations altogether would be a problematic solution: There is after all a vast amount of linguistic data that has led to the development of abstractionist theories to begin with. The pioneering findings of historical linguistics would not have been possible without the stability of lexical classes (as noted for example by Bermúdez-Otero, 2015). Sound changes such as Grimm’s Law operated on sound categories shared by multiple lexical items, rather than, or at least in addition to, individual words. Pronunciation of neologisms and loan word assimilations, where sounds of the native language are employed, also attest to the existence of a level of abstract representation (Pierrehumbert, 2001). Consequently, recognizing the need for both abstract and phonetically rich representations, several descriptions of a viable compromise—a hybrid view of representation—have been proposed (Ernestus, 2014; Goldinger, 2007; Nguyen, 2012; Pierrehumbert, 2002, 2006, 2016).

Indeed, research on the perception of word-final /t/ allophony points to the role of both abstract (Sumner & Samuel, 2005) and phonetically rich (Garellek, 2011) representations. Investigating the variation in word-final /t/, Sumner and Samuel (2005) found that all three variants of final /t/ they analyzed, namely [t], [ʔt̚], and [ʔ], cause semantic priming (e.g., [fluːt], [fluːʔt̚], and [fluːʔ] all activate /fluːt/ flute and prime music). If the non-canonical [ʔt̚] and [ʔ] were not stored, one would expect an advantage of the canonical form [t], as processing the other two variants would involve additional computation or relying on non-canonical cues. If they were stored, one would expect [ʔt̚], the most frequent variant of /t/ prepausally to show an advantage. Neither effect was found: All three variants did better than a mismatched form (e.g., [fluːs]). This null effect is not conclusive with regard to storage. The absence of evidence of a difference cannot be taken as evidence of absence of such a difference: It might be too small to detect with the semantic priming paradigm used by Sumner and Samuel (2005). Indeed, using a phoneme monitoring task, Garellek (2011) found that [ʔt] tokens were recognized faster than canonical [t], suggesting that a more frequent variant of final /t/ is recognized more easily. Sumner and Samuel (2005) further found that only the canonical form, that is [t], shows a long-term priming effect (e.g., [fluːt], but not [fluːʔt̚] or [fluːʔ], primes [fluːt] in a lexical-decision and in a new-old recognition task). This effect, present across two different tasks, supports the primary role of the canonical form, that is of a form with only and all the contrastive features of English phonology, in long-term priming. While this finding of Sumner and Samuel points to the role of abstract representations of final /t/ in perception, the effect found by Eddington and Channer (2010) and confirmed here bolsters the support for phonetically-rich representations of the same final /t/ in speech production.

Taken together, these two effects support a hybrid model of phonological storage, with both abstract and phonetically rich levels of representation, relevant for different types of processing. To take a brief look at the literature on speech recognition, the role of two levels was demonstrated by several studies (McLennan, Luce, & Charles-Luce, 2003; Vitevitch & Luce, 1998).⁵ Tasks involving strong lexical competition (using words with high neighborhood densities, or hard lexical-decision tasks), phonologically ambiguous input, and ample time allowed for recognition, seem to be tapping the abstract level. Other tasks, such as auditory naming, easy-discrimination lexical decision, little time allowed for recognition, seem to be tapping the phonetically rich level. Several hybrid models of phonological storage, promising to accommodate such findings, have been proposed (cf. Ernestus, 2014; Goldinger, 2007; Nguyen, 2012; Pierrehumbert, 2002, 2006, 2016). For the effect confirmed in the present study, detailed representations with a glottal stop and a flap need to be postulated. Interestingly, since in connected speech both of these phonetically quite different variants can occur in the same context, it is clear that their abstract representation with /t/ cannot explain their usage patterns. Instead, phonetically detailed representations become more entrenched with use. This interpretation is consistent with the finding of Dilley and Pitt (2007) that high-frequency /t/-final words show more variability in the realization of /t/ than low-frequency words. Higher lexical frequency entails frequent occurrence in a variety of phonological contexts, and different contexts favor different allophones. The more often a particular word occurs in environments favoring one of the representations, the more entrenched, and the more likely to be used that phonetic form becomes. Hence, a word typically occurring before consonants shows higher glottaling rates, even in prevocalic position. But for the entrenchment in preconsonantal position to take place, the production of [ʔ] must be linked to an activation of an abstract category /t/. Repeatedly, the phoneme finds itself in preconsonantal context, which favors glottaling. Such occurrences strengthen the links between the phoneme in this word, and its allophone [ʔ]. The allophone is then activated even in environments which do not favor this allophone. The stronger the link, the higher the likelihood of glottaling. A functionally-oriented take on t-glottaling, namely that the glottal constriction enhances the recognition of /t/-final words by strengthening the cues to the voicelessness of the coda, was not substantiated (Chong & Garellek, 2018; cf. Seyfarth & Garellek, 2015). Instead, Chong and Garellek (2018) found that glottaling simply does not inhibit the recognition of /t/-final words, while it does inhibit the recognition of /d/-final words. Glottaling, it seems, can spread through the /t/-final words as it does not incur any cost here. These, and other factors influencing t-glottaling can be seen as forming part of the nexus of selection forces on the spread of t-glottaling, one of them being contextual frequency. And, to conclude, while prevocalic t-glottaling presents another case where the ‘abstract only’ view of phonological representation is insufficient (cf. Baayen, 2007), a hybrid model, rather than an exemplar model of phonological storage is best equipped to accommodate the finding that the rate of glottaling of prevocalic /t/s at word boundaries increases with the frequency of occurrence of a given word before consonant-initial words.

Funding Information

This research was supported by the National Science Centre (Poland) grant No. UMO-2017/26/D/HS2/00027.

Competing Interests

The author has no competing interests to declare.