Empirical work on English CSD has played a role in the development of a range of different theoretical frameworks; accounts of what linguistic mechanisms lead to deletion are varied. While the empirical facts are clear on the robustness and universality of the FSE, explanations for its source are equally varied.

The earliest quantitative work on CSD is found in Labov et al. (1968), a foundational study in the Labovian variable rule tradition. In this framework, the rule leading to deletion of a coronal stop can be weighted in its probability of application according to the substance of other contextual elements, such as morpheme boundaries or following segments. The formulation of CSD as a Labovian variable rule thus stipulates (by writing into the rule) that the rule applies at higher rates before consonants than before vowels. Of course, it is widely understood that such an approach can achieve great descriptive precision but could equally well describe a counterfactual state of affairs in which CSD applies more often before vowels than consonants. Such flexibility may be appropriate in cases of social conditioning where the facts could be arbitrary, but the overwhelming evidence for the universality of the basic FSE on CSD leads us to seek a deeper explanation.

One such explanation that has been put forward is that the FSE is due to syllabification bleeding deletion (Guy 1991). When the next word begins with a vowel, the word-final coronal stop that would be targeted by deletion is “saved” by recruitment as an onset consonant for the following word. One piece of evidence that Guy puts forward to support this proposal is a discrepancy between deletion rates before /l/ and /r/, which in earlier work had been combined as a natural class. In Guy’s data from several varieties of American English, deletion is frequent before /l/, which is disallowed as the second element in an English onset cluster, but rare before /r/, which may become part of a well-formed onset cluster with /t/ or /d/ (as in train or drive). In Guy’s Lexical Phonology analysis, CSD is the result of an autosegmental delinking rule that applies at each level of the morphophonology and is followed by stray erasure of unlinked segments at the end of the derivation. One question that this analysis doesn’t resolve is why there should be any pre-vocalic deletion at all; Guy suggests briefly that “syllabification may itself be a variable rule” (1991: 18) but does not pursue the implications of that possibility. Nonetheless, his syllabification-based explanation for the FSE has intuitive appeal and has been reworked for analyses of CSD in other phonological frameworks such as Optimality Theory (Kiparsky 1993; Reynolds 1994).

A quite different perspective on CSD comes from Articulatory Phonology (Browman & Goldstein 1992), in which the basic units of speech, gestures, are “primitive actions of the vocal tract articulators” (Browman & Goldstein 1989: 201). In Articulatory Phonology, the actions of different articulators (such as lips, tongue tip, or velum) are coordinated across different tiers in the production of continuous speech, with gestures on different tiers naturally exhibiting temporal overlap. The extent and perceptual consequences of such overlap are, in this framework, a major source of phonological variation, and can elegantly account for phenomena such as deletion. Browman and Goldstein observe that “with sufficient overlap, one gesture may completely obscure the other acoustically, rendering it inaudible” (Browman & Goldstein 1989: 215). They give the example of the phrase “perfect memory,” in which the /t/ is “hidden” by the labial gesture of the /m/ despite still being fully articulated by the tongue tip. The Articulatory Phonology account of CSD is supported by the observation that CSD exhibits phonetic gradience in both the acoustic signal (Temple 2009) and the articulatory gesture (Purse & Turk 2016). In Articulatory Phonology, the FSE has a natural interpretation: it is consonantal gestures, with their extreme or even complete constriction of the vocal tract, that produce the gestural masking that gives rise to the percept of deletion. Vocalic gestures do not hide consonant gestures in the same way; rather, “consonant articulations are superimposed on continuous vowel articulations” (Browman & Goldstein 1990: 10). The FSE, then, is not an adjustment of CSD rates in different phonological contexts, but in fact is one and the same phenomenon as CSD: masking of word-final coronal stops by the consonant that follows.

The observation of acoustic and articulatory gradience in CSD is also consistent with what Jurafsky et al. (2001) call the probabilistic reduction hypothesis, under which CSD may be treated as a gradient phonetic reduction process rather than a probabilistic phonological rule with binary outcomes or an epiphenomenon of gestural overlap. Under the probabilistic reduction hypothesis, words are more likely to undergo various reduction processes when they are more predictable in their context. While CSD has been a source of evidence in favor of the probabilistic reduction hypothesis and related usage-based accounts (Jurafsky et al. 2001; Gahl & Garnsey 2004; Ernestus 2014), this hypothesis does not in itself straightforwardly predict the FSE. In principle, perhaps, distributional asymmetries between consonants and vowels across contextual predictabilities could be used to construct a predictability-based account of the FSE. Probabilistic reduction is relevant to this paper, though, because contextual predictability is a factor that may influence production planning.

In Section 4.1, I will return to the nature of the FSE and argue that its interaction with syntactic boundaries provides new evidence in favor of an analysis that is at least broadly similar to Guy’s syllabification proposal.

An early paper demonstrating that syntactic boundaries can interact with phonological rule application is Cooper et al. (1977), a study of the trochaic shortening rule in which “the duration of a stressed syllable that is immediately followed by an unstressed syllable (therefore forming a bisyllabic trochee) is normally shortened relative to its duration as a monosyllable” (Cooper et al. 1977: 1314). In a laboratory reading experiment, they compare the duration of the syllable /klɪn/ in “Clinton till,” where trochaic shortening applies within the word Clinton, with the duration of the same syllable in “Clint until,” where the domain for trochaic shortening extends across the word boundary. They also vary the structure of the sentences to change the syntactic relationship between Clint/Clinton and the following prepositional phrase. They observe that for all the boundary types they test, /klɪn/ is shorter in Clinton till than in Clint until, and suggest that the syntactic boundaries block application of trochaic shortening. While this result could profitably be connected to the expansive literature on syntactic locality or prosodic domain restrictions on phonological rules (Chomsky & Halle 1968; Selkirk 1986), Cooper et al. appeal specifically to production planning to explain the effect, writing, “For cases in which a syntactic boundary blocks a rule normally applicable across word boundaries, we can infer that the boundary acts as a juncture in the speaker’s processing, prohibiting any following information from influencing segments preceding the boundary” (Cooper et al. 1977: 1314). In a second experiment, they replicate the result using different pairs of test sentences and additionally observe that the duration difference does not obtain when the unstressed syllable that triggers shortening is in a noun phrase object (“Horace brought Clint an enormous turtle” versus “Horace brought Clinton enormous turtles”). In other words, the boundary preceding a direct object is not strong enough to block rule application. After considering various possibilities, they operationalize the relevant boundary strength differences in terms of branching depth. Finally, they briefly report similar results from a study of variable palatalization of /d/ across word boundaries before /j/, as in “had yet” being pronounced /hɑdʒjɛt/.

Wagner (2011) reports a laboratory production experiment about the English alternation between /ɪn/ and /ɪŋ/ for the <–ing> suffix. Use of the coronal form instead of the velar is supposed to be more likely when the following segment is coronal, an effect which Wagner hypothesizes should be sensitive to the syntactic relationship between the target word and the following segment. In a sentence like, “While the man was reading, a book fell off the table,” reading and a book are in a syntactically non-local relationship. This is in contrast to a sentence where a book is a direct object and thus syntactically close to reading, such as, “While the man was reading a book, a glass fell off the table.” In Wagner’s experiment, participants read aloud sentences that vary both in the syntactic juncture following the target <–ing> suffix and in whether the article used is definite or indefinite. The latter manipulation evinces the expected FSE: when a book is replaced by the book, participants use /ɪn/ more often as a result of the coronal /ð/. In the sentences where a/the book is syntactically non-local to reading as opposed to being a direct object, though, that FSE is significantly weaker. Moreover, the size of the FSE has a gradient negative relationship with the duration of the following article. Wagner interprets the duration of the article as an acoustic proxy for the likelihood that the article had been phonologically encoded when the ING variable is produced. He proposes that cases where the following word is not yet encoded bleed the FSE on <–ing> alternant choice, and that such cases are more likely across stronger syntactic and/or prosodic boundaries.

Tanner et al. (2017) spell out a number of predictions of the PPH for CSD in particular, then test these predictions on British English CSD data taken from a conversational speech corpus. Differently from the current study, which focuses on cases with no intervening pause between the coronal stop and the following segment, they are particularly interested in the gradient influence of intervening pause duration. As predicted, they find that the FSE is weaker when an intervening pause is longer. This result is similar to the gradient influence of article duration on the FSE on <–ing> choice in Wagner (2011), and is consistent with the PPH under the assumption that longer pauses may reflect cases where upcoming material is not yet planned (O’Connell et al. 1969; Butterworth 1975; Ferreira 2007; Fraundorf & Watson 2014). They also find that both lexical frequency and speech rate, two factors also included in the current study, modulate the FSE. The direction of the frequency interaction is such that the FSE is weaker in higher frequency target words, which may reflect the greater likelihood that easily-retrievable high frequency words have already been planned before the following word is encoded (Oldfield & Wingfield 1965; Jescheniak & Levelt 1994; Alario et al. 2002: inter alia). The nature of the interaction between speech rate and following segment is non-linear, in that the FSE is minimized in both particularly slow and particularly fast speech. In addition to pause duration, lexical frequency, and speech rate, Tanner et al. include in their models a measure of the conditional probability of the following word given the target word: an unpredictable upcoming word, just like a globally infrequent one, should prove more difficult to encode and therefore be associated with co-presence failure. Modeling the effect of following word conditional probability, though, proves difficult due to high interspeaker variability even in their quite large dataset; because the current study is smaller, and especially has much less data per speaker, I do not pursue replication of this predictor. Tanner et al. point out that the absence of syntactic annotation in their dataset prevents them from directly investigating the influence of syntactic locality. The current study is both an attempt to replicate Tanner et al.’s (2017) results on a quite different English variety, and a novel implementation of Wagner’s (2011) syntactic locality predictions in corpus data. Tanner et al. benefit from a large sample size but lack syntactic information; I make the trade-off in the other direction and prioritize the manual extraction of syntactically-detailed information at the partial expense of sample size.

Kilbourn-Ceron (2017b) reports three case studies aimed at testing the predictions of the PPH on quantitative data from different variable processes. The first study, further developed in Kilbourn-Ceron & Sonderegger (2018), uses data from the Corpus of Spontaneous Japanese (Maekawa et al. 2000) to look at Tokyo Japanese high vowel devoicing between voiceless consonants and before pauses. Previous work had shown that this devoicing process (which Kilbourn-Ceron and Sonderegger ultimately argue is two separate processes) is less likely across morpheme and word boundaries than word-internally (Varden 1998; Imai 2004). Kilbourn-Ceron and Sonderegger find that not only is devoicing more likely across word boundaries, but also its likelihood drops as the pause between words gets longer. Parallel to Tanner et al. (2017), they argue that this is consistent with the PPH because the devoicing rule depends on a following voiceless consonant which is less likely to have been planned in time to trigger devoicing when it is across a stronger prosodic boundary.

In Kilbourn-Ceron’s second case study (also reported in Kilbourn-Ceron et al. 2016 and Kilbourn-Ceron et al. 2017), she and her colleagues investigate contextual effects on the intervocalic flapping of coronal stops, which is variable across word boundaries in American English. Stimuli in a laboratory production experiment contain a flappable stop in a nonce word embedded in an English sentence, with syntactic boundary location manipulations comparable to those from Wagner (2011). They use pre-flap vowel duration to control for pre-boundary lengthening and conclude that stronger syntactic boundaries have an effect of reducing flap likelihood above and beyond prosodic boundary strength. They also conduct a corpus study of the same phenomenon using data from the Buckeye Corpus (Pitt et al. 2007). The corpus data produces a significant positive association between flapping probability and following word frequency. Because flapping is a process whose planning requires intimate involvement with the first segment of the following word, the predictability of the following word as measured by lexical frequency functions as a planning proxy.

The same logic underpins the final case study in Kilbourn-Ceron’s dissertation (also reported in Kilbourn-Ceron 2017a): variable French liaison, when certain consonants are realized word-finally only before following vowels. She shows that in the Phonologie du Français Contemporain corpus (Durand & Lyche 2003), global lexical frequency of both the first and second word as well as predictability of the second word given the first word positively correlate with liaison rates. This is expected under the PPH because high predictability is expected to facilitate access of phonological form (Oldfield & Wingfield 1965; Jescheniak & Levelt 1994) and so decrease the likelihood of co-presence failure. This is also the only previous study that has successfully linked PPH predictions for corpus data to questions of syntactic structure: the liaison rate is much higher across a weaker syntactic boundary (Adj–Noun as opposed to Noun–Adj).

Finally, a paper in which the predictions of the PPH find perhaps less support is MacKenzie’s (2016) study of variable English auxiliary contraction. The following context effect at play in this case is not segmental in nature but rather is the sensitivity of contraction to the grammatical category of the following constituent. This following category effect, robust through it is with respect to the probability of contraction, does not interact with duration of the following word. MacKenzie suggests that if the planning of syntactic structure and lexical content takes place earlier than phonological encoding, as many sentence production models propose (Garrett 1975; Sternberg et al. 1978; Bock & Levelt 1994), then the following constituent’s syntactic identity may invariably be in place prior to the speaker’s choice of auxiliary allomorph. Her paper, then, can be thought of not as a refutation of the PPH but rather a refinement of it: only a planning proxy at the relevant planning level should be expected to modulate following context effects.

The current study tests whether the FSE on CSD interacts with each of four other contextual factors, all of which may be considered potentially relevant planning proxies because previous work on the PPH has suggested they may modulate following context effects:

This study was primarily designed to investigate the role of syntactic boundaries in conversational CSD. The reason I focus on the syntactic question is that, while important evidence for the PPH comes from read speech tasks in which syntactic differences are the central manipulation of interest, previous studies investigating the PPH in conversational speech have not been able to incorporate any syntactic information. A goal of this paper is thus to look for evidence that syntactic structure modulates following context effects in conversational speech, while still taking into account other planning proxies that may be related or independently active. Fully disentangling the influence of syntax and prosody on phonological variation would require a larger dataset annotated with both syntactic and prosodic information. The focus on syntactic boundaries in this paper is not intended to imply that the only relevant structure is syntactic. Given that research on the PPH is relatively new and still developing, my position in this paper is that syntax and prosody both serve merely as proxies for the variable scope of production planning, which we cannot observe directly. The relationship between syntactic and prosodic structure is, of course, a broad area of inquiry and will continue to be of interest in the study of phonological variation.

The discussion in Section 1.2 above provided background on each of these predictors; I briefly recap the motivations for the inclusion of each, then expand on the role of duration in this study because it diverges somewhat from previous similar studies.

Stronger syntactic boundaries are predicted to reduce the FSE on the assumption that direct objects are more likely to have been planned in the same planning window as the verbs that select them, whereas less local adjuncts or separate clauses are less likely to have been planned simultaneously with the target word (see Wheeldon 2012 for an overview of evidence on the syntactic scope of planning). I discuss the syntactic coding and selection of strong and weak syntactic boundary contexts at some length in Section 2.2. High frequency words are expected be easily accessible and therefore planned early (Oldfield & Wingfield 1965; Jescheniak & Levelt 1994), so co-presence failures and subsequent weakening of the FSE should be associated with high frequency words. Speech rate might influence FSEs by dynamically adjusting the size of the planning window; Wagner et al. (2010) argue that the window for planning gets narrower at faster speech rates, which would suggest that faster speech might exhibit weaker cross-word effects. Speech rate can be thought of in absolute terms (whether someone is a slow or fast talker) or relative terms (whether a stretch of speech is especially slow or fast for a particular talker); following Tanner et al. (2017), I calculate these separately as characteristic speech rate and speech rate deviation, and focus on the latter in terms of its interaction with the FSE.

Finally, stronger prosodic boundaries are predicted to reduce the FSE on the premise that planning units may in fact be prosodic units (Keating & Shattuck-Hufnagel 2002), such that prosodic breaks might correlate with unplanned upcoming material. The precise proxy used for prosodic boundary strength has varied across previous studies of the PPH. Typically boundary-adjacent durational measures are used, taking advantage of the general phenomenon that strong prosodic boundaries tend to induce phonetic fortition of adjacent segments along a number of dimensions; see Keating (2006), Fletcher (2010), and Cho (2011) for relatively recent overviews of the sizeable literature on both domain-final and domain-initial strengthening.

However, choosing an appropriate duration measure is not a trivial task. Kilbourn-Ceron et al. (2017) use pre-boundary (domain-final), pre-flap vowel lengthening as a prosodic boundary proxy in their study of /t/-flapping, while Tanner et al. (2017) use pause duration at the boundary in their corpus study of CSD. Since CSD targets the final elements of consonant clusters in particular, I avoid taking preceding segment duration as a proxy for boundary strength, because I expect this durational measure to be sensitive to the presence or absence of the coronal stop (and thus in many cases, cluster versus singleton consonant status of the preceding segment). And, as discussed in Section 2.3, the existing dataset on which this study was built excludes information about following segments across pauses of any non-negligible length. Having ruled out pre-boundary and at-boundary durational measures, I turn to post-boundary (domain-initial) segment duration as a potential reflection of prosodic boundary strength.

The use of post-boundary segment duration as a prosodic proxy partly follows Wagner’s (2011) use of domain-initial function word reduction in his experimental work on PPH effects on <–ing>. The advantage of the experimental context in that case is that there were only two following words in use, “a” and “the.” Doing speaker-specific word duration normalization across the many possible following words in the corpus data is not viable. By-speaker segment duration normalization is more achievable, and is further motivated by the general finding that domain-initials strengthening effects are most strongly apparent on the first segment after the boundary (Byrd et al. 2006; Byrd & Choi 2010: inter alia). But the use of following segment duration poses its own challenges. While there is strong and cross-linguistically diverse evidence for domain-initial consonant lengthening (Fougeron 2001), the facts about domain-initial vowel lengthening are somewhat murkier. While it has been claimed that domain-initial vowels exhibit minimal if any lengthening, many instances of this claim have been made on the basis of vowels in a #CV context, which are not, strictly speaking, linearly domain-initial (e.g. Cho & Keating 2001). Fougeron (2001) finds only one out of four test subjects to exhibit domain-initial vowel lengthening in French. More recent papers on Korean from Lee (2007) and Cho et al. (2014), both comparing C#V and #CV contexts, diverge in their results. Lee finds that word-initial vowels are shorter when they follow stronger prosodic boundaries, whereas Cho et al. find domain-initial lengthening for both vowels and consonants. I return to the question of consonant–vowel lengthening differences, and their ramifications for this study, in Section 2.3.

The predictors of CSD are many and varied, posing a statistical modeling challenge for a study of yet more predictors and their interactions. A guiding principle of this study was to seek a reasonable compromise between two competing goals: 1) to reduce the number of predictors and interaction terms to avoid an overly complex model that is difficult to interpret; and 2) to retain enough data that real effects will be apparent. For several of the predictors that are known to influence CSD, I chose a strategy of including only tokens with one or two values of the predictor prior to manual coding of syntactic structure. I limited my attention to the two largest morphological categories typically distinguished in CSD studies: monomorphemes, such as “act” or “blend,” or regular preterite forms like “walked” or “zoned.” Passive participles, although often combined with the preterites, are excluded in this study, as are all irregular past tense forms like “went,” “swept,” “lost,” or “cost.” All observations included in this dataset are monosyllabic to improve consistency across observations in lexical stress patterns. They also all have a homovoiced final cluster (voiceless consonant plus /t/ or voiced consonant plus /d/)—final clusters in preterite forms are, by voicing assimilation rule, all homovoiced, so I limit the monomorphemes included to match. The segment preceding the coronal stop is another well-established determinant of CSD rates, if not an especially strong one (Guy 1980). Observations of CSD are distributed fairly evenly across a larger number of preceding segments, so the narrowing approach of focusing on a single category in this case would result in an insufficient amount of data. Preceding segment is instead controlled through the statistical analysis reported in Section 3, with six types of preceding segment distinguished: stop, fricative, liquid, nasal, sibilant, affricate. Of these, previous work exhibits minor disagreements but suggests that sibilants should favor deletion most highly and liquids the least (Labov 1989; Patrick 1991).

After restricting the existing data set in these ways, there were a total 2,488 tokens remaining in the dataset to be coded for syntactic structure.

The CSD-coded tokens were matched back up to the original orthographic transcript using a Python script, then the dependent variable outcome codes hidden until after all syntactic coding had taken place. The goal of the syntactic coding was to extract the two subsets of the data that, when juxtaposed, represent maximal differentiation of boundary strength, rather than to exhaustively characterize each token’s syntactic structure. Table 1 lists and provides examples of the structural boundary types that were used to achieve this goal. Following the results from Cooper et al. (1977), I make a comparison between weak boundary sentences, where the following segment begins a direct object, and strong boundary sentences, where there is a greater hierarchical distance between the coronal stop and the following segment. The sentence types included were selected as being relatively straightforward to define and detect while occupying near-endpoints to a possible continuum of boundary strengths. The first two categories in Table 1 are those where the coronal stop falls right before a juncture between two separate independent (matrix) clauses, with or without a conjunction (Matrix CP + Matrix CP; Matrix CP + Conjunction + Matrix CP). The second two categories are those where there is an adjunct, typically a prepositional phrase or adverbial phrase, that is either preposed (High adjunct + Matrix CP) or right-adjoined higher than the verb phrase containing the target word (Matrix CP + High adjunct). The latter are often similar to the sentence types in which Cooper et al. (1977) identified the syntactic blocking of trochaic shortening. Taken together, I treat the total of 269 utterances falling into these four categories as having strong syntactic boundaries. In the remaining category, I include only cases in which the coronal stop is at the end of a verb (whether monomorphemic or past tense) and the following segment begins a direct object to that verb. This is the boundary type that Cooper et al. (1977) found did not block cross-word rule application. There are a total of 567 such utterances in the dataset, which I treat as having weak syntactic boundaries. The dataset for analysis, after these rounds of exclusions and subsetting, contains 836 observations from 96 unique speakers and 261 unique words. The distribution of word types and following segments across the syntactic boundary types are given in Table 2.

An alternative characterization of the weak versus strong syntactic boundary distinction made here is that of syntactic locality versus non-locality. Framing the contrast in locality terms would be consistent with the discussion from Wagner (2012). However, it is beyond the scope of this paper to assess whether the active distinction is a binary local/non-local one or a more gradient measure of hierarchical distance. I do not attempt to quantify boundary strength (for example, by counting branches as Cooper et al. 1977 suggest) because to do so would require imposing a great deal of syntactic theory without knowing exactly what quantification of boundary strength is relevant to phonological planning. I avoid locality-related terminology because it implies a stricter claim than the boundary strength terminology: a non-local relationship could fairly be described as involving a strong syntactic boundary, but it may not be the case that all syntactically non-local configurations involve boundaries strong enough to interfere with production planning. It remains quite possible that locality is in fact the relevant distinction; future experimental work might pit these possibilities against each other rather than deliberately confounding them.

The first seven lines of Table 3 after the intercept report the familiar effects of morphological class and phonological context on CSD. Monomorphemes show more deletion than preterite forms ( β^=0.32, p=0.030 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 0.32,\ p = 0.030 \] \end{document} ). Following vowels have a large and significant negative effect on deletion rate ( β^=−4.06, p<0.001 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = - 4.06,\ p < 0.001 \] \end{document} ). This, of course, is the FSE, and by far the largest effect in the model. While fricatives and liquids have previously been reported to be less favorable to deletion than stops, here they do not differ significantly from stops ( β ^ =−0.41, p = 0.355 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = - 0.41,\ p = 0.355 \] \end{document} ; β ^ =−0.10, p=0.755 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = - 0.10,\ p = 0.755 \] \end{document} respectively), although their coefficients are still in the expected direction. The three preceding segment types that were expected to favor deletion more than stops, though, do indeed each have a significant positive effect on deletion rate compared to the reference stop category (nasals: β ^ =1.23, p < 0.001 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 1.23,\ p < 0.001 \] \end{document} ; sibilants: β ^ =0.89, p = 0.010 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 0.89,\ p = 0.010 \] \end{document} ; affricates: β ^ =2.60, p < 0.001 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 2.60,\ p < 0.001 \] \end{document} ). These results are broadly consistent with many previous studies, and indeed with prior analysis of the larger dataset that these data were drawn from (Tamminga 2014; 2016).

Neither characteristic speech rate nor speech rate deviation has a significant effect on CSD in this dataset ( β ^ =−0.01, p = 0.956 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = - 0.01,\ p = 0.956 \] \end{document} ; β ^ =0.10, p = 0.549 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 0.10,\ p = 0.549 \] \end{document} , respectively). Lexical frequency also does not have a significant main effect on CSD ( β ^ =0.18, p = 0.272 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 0.18,\ p = 0.272 \] \end{document} ), nor does following segment duration ( β ^ =−0.23, p = 0.375 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = - 0.23,\ p = 0.375 \] \end{document} ). Finally, there is not a significant main effect of syntactic boundary strength ( β ^ =−0.32, p = 0.387 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = - 0.32,\ p = 0.387 \] \end{document} ); bear in mind that this means there is not a significant difference between the pre-consonantal deletion rates across strong and weak syntactic boundaries.

The model contains five theoretically-motivated interaction terms: the two-way interaction of the following segment with each of the four planning-relevant factors plus the three-way interaction of following segment, following segment duration, and syntactic boundary. Of these, only the interaction of following segment and syntactic boundary is significant: across a strong syntactic boundary, there is more deletion before a following vowel than there is across a weak syntactic boundary ( β ^ =1.12, p=0.013 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 1.12,\ p = 0.013 \] \end{document} ). Figure 2 shows the observed (not predicted) differences in CSD rate across the cross-tabulated categories of these two predictors. None of the other interaction terms are statistically significant (following vowel × following segment duration: β ^ =−0.02, p=0.959 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = - 0.02,\ p = 0.959 \] \end{document} ; following vowel × lexical frequency: β ^ =0.39, p=0.088 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 0.39,\ p = 0.088 \] \end{document} ; following vowel × speech rate deviation: β^=0.00, p=0.996 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 0.00,\ p = 0.996 \] \end{document} ; following vowel × strong boundary × following segment duration: β ^ =0.09, p=0.855 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \beta = 0.09,\ p = 0.855 \] \end{document} ).

As a supplement to the Wald tests of the significance of individual predictors reported in Table 3, I also used a series of likelihood ratio tests (LRTs) to assess whether the model as a whole performed better with or without the interactions of theoretical interest. Each LRT compares a model with an excluded term to the full model. The first LRT compares the full model to a model excluding the three-way interaction. The subsequent LRTs test the effect of excluding each two-way interaction in turn, necessarily also excluding the three-way interaction when testing the two-way interactions that are nested in it. The chi-squared values and p-values from these LRTs, which are consistent with the results from the Wald tests reported in Table 3, are given in Table 4.

Beyond observing the existence of an interaction between syntactic boundary strength and following segment, the results in Section 3 also provide evidence that this interaction is asymmetrical. The rate of CSD before consonants is minimally sensitive to boundary strength, as can be seen in the lack of significant main effect for boundary strength when following segment is at its reference level of consonant in the regression model. These statistical results reflect a pattern that is equally apparent in the observed data, as graphed in Figure 2. The observed rate of CSD before consonants is 81% across weak syntactic boundaries and 78% across strong syntactic boundaries. The interaction with syntactic boundary strength arises primarily in the context of following vowels: the observed rate of pre-vocalic CSD goes up from 11% across weak syntactic boundaries to 33% across strong ones.

One way to think of this asymmetry is that the deletion-inhibiting effect of a following vowel is reduced in the context of a strong intervening syntactic boundary. This suggests interruption of a process that applies only in pre-vocalic environments and not in pre-consonantal environments, pointing us back towards syllabification-based explanations for the FSE (Guy 1991). To recap from Section 1.1, the essence of the proposal is that word-final coronal stops can be syllabified as onsets to a following vowel, which protects them from deletion. Stops preceding word-initial consonants cannot be resyllabified and therefore are always vulnerable to CSD. If this is correct, it is the pre-vocalic environments specifically that should be affected by variability in production planning. If a following vowel is not yet encoded (a co-presence failure), it cannot host the coronal stop as its onset, and a stop that might otherwise have been protected and therefore retained is instead subject to variable CSD. When there is a co-presence failure with a following consonant, however, it does not affect CSD rate because the consonant would have played no role in CSD rate even if it had been present at the right moment.

The other accounts for the FSE reviewed in Section 1.1 do not provide the same ready explanation for this asymmetry. Under an analysis where the presence of a consonant or vowel is equally “active” in influencing the likelihood of CSD (for example, in a variable rule with stipulated probabilities for those environments), we would expect co-presence failures to be distributed evenly across pre-consonantal and pre-vocalic deletion opportunities. We would thus expect the reduction of the FSE to be symmetric, with both a higher deletion rate before vowels and a lower deletion rate before consonants in the strong syntactic boundary context. In an Articulatory Phonology framework, we first of all might expect that any boundary-interacting effects should be more responsive to prosodic boundary strength than syntactic boundary strength, as Articulatory Phonology easily encompasses phrasal phonology but has no mechanism for connecting to the syntax. This point, though, is susceptible to the caveat discussed in the previous section, that the syntactic boundary may actually be serving as a prosodic boundary proxy. But even if the relevant boundary is the prosodic one, the gestural masking account of CSD in particular makes the wrong prediction about the asymmetry of the FSE’s sensitivity to boundary strength. Recall that gestural masking is consonant-driven, rather than vowel-driven. Greater gestural overlap across weaker boundaries should be associated with a greater perceptual deletion in pre-consonantal environments but not a shift in perceptual deletion in pre-vocalic environments.

A wrinkle here is that it is possible to formulate a syllabification-like explanation for the FSE within Articulatory Phonology; as Cho et al. point out about C#V strings within a phrase, “C and V gestures may reorganize temporally as having an in-phase coupling relationship” (2014: 98). Furthermore, such an account can also capture the failure of syllabification across strong boundaries; Cho et al. argue that “an IP boundary is likely to block C and V gestures in the C#V context from reorganizing temporally” (2014: 98). To say that the basic gestural-masking explanation of CSD does not make the correct prediction for this data is not to argue that Articulatory Phonology as a framework cannot account for the set of facts here.