The present study used articulatory data from speakers of Moroccan Arabic (MA) gathered using 3D electromagnetic articulography (‘EMA’; Hoole, Zierdt, & Geng, 2003).

MA is a highly appropriate language for studying timing in consonant clusters since it permits a complex set of consonant combinations. Looking only at two-consonant stop-stop sequences; labial, coronal, and dorsal places of articulation are attested in all six combinations word-initially, word-medially, and word-finally except for word-final coronal-labial combinations, as shown in Table 1. Several manners of coronal consonants—liquids, a rhotic tap, stops, and fricatives—are also widely found as members of two-consonant clusters. The freedom of clustering permitted in MA therefore offers a particularly apt case study for testing hypotheses regarding overlap, since it is possible within-speaker to independently control for and test the effects of several different factors affecting overlap.

There are two aspects of articulation that are crucial to the proposal made by Jun (2004): the ‘rapidity’ of an articulation (which is a function of the ‘inherent velocity’ of the primary articulator), and overlap. However, no quantification of either of these terms is made explicit by Jun (2004), and there are multiple ways to quantify both terms. We motivate two different quantifications for each term that we used to refine and test the present hypotheses.

In order to discuss these quantifications, we first discuss how articulator movements themselves were identified and quantified. The articulator movement measured for each consonant was indexed by the EMA receiver corresponding to the consonant’s primary oral articulator—lower lip: [b]; tongue tip: [d, t]; tongue body: [g, k]. Following Gafos (2002, p. 296), we assumed that the oral gesture of a consonant drives the timing relations among consonants.

Each gesture can be characterized by certain temporal landmarks, shown in Figure 3. The Onset of movement is the point in time where the articulator starts moving toward its required constriction, Target is the point in time when that constriction is achieved, and Release is the point in time when the articulator moves away from the constriction. The Plateau of the articulation is the interval between Target and Release when the articulator is maintaining the required constriction. These landmarks were calculated using MATLAB-based code for analyzing EMA data (‘MVIEW’), developed at Haskins Laboratories by Mark Tiede. Onsets were identified algorithmically as the point at which the EMA sensor exceeded 20% of the maximum tangential velocity of the articulator during the closing phase, Targets were identified as the point at which the EMA sensor subsequently went below 20% of the same maximum tangential velocity, and Releases were identified as the point at which the EMA sensor exceeded 20% of the following maximum tangential velocity (comparable to the methods used by, e.g., Chitoran, Goldstein, & Byrd, 2002; Gafos, Hoole, Roon, & Zeroual, 2010; Oliveira & Teixeira, 2007, and many others). Further technical details about the acquisition and processing of the EMA data are included in the Methods section below.

Since the primary goal of the present study was to investigate the potential influence of relative rapidity of articulation on overlap, other known influences on overlap in clusters had to be taken into account. We summarize below some known influences on overlap in general, as well as on some specific findings from Gafos et al. (2010), who reported on cluster overlap from a different subset of the data from two of the speakers analyzed in the present study.

Word-initial clusters are typically less overlapped than word-medial, intervocalic clusters. This effect has been observed for English (Byrd, 1996; Hardcastle, 1985), Tsou (Wright, 1996), and Georgian (Chitoran et al., 2002). There are two perception-based motivations for this effect given by Chitoran et al. (2002). First, word-initial clusters are less overlapped because they may also be utterance-initial and therefore lack a preceding vowel to provide cues to help in the identification of the first consonant in the cluster (see Redford & Diehl, 1999, for more discussion). Second, lexical access relies heavily on word-initial phonetic detail (Marslen-Wilson, 1987). Gafos et al. (2010) compared overlap in stop-stop clusters for two MA speakers. Word position effects were found for Speaker 1 in that word-initial clusters were significantly less overlapped than word-medial and word-final clusters. The overlap in word-medial clusters was not significantly different from word-final clusters. Speaker 2 showed no word position effects.

Previous studies (Byrd, 1992, 1996; Chitoran et al., 2002; Hardcastle & Roach, 1979; Son, 2008; Surprenant & Goldstein, 1998; Zsiga, 1994) have shown that front-to-back clusters (that is, clusters where the place of articulation of C1 is anterior to that of C2, e.g., [tk], [pt]) tend to show more overlap on average than back-to-front clusters (e.g., [kt], [tp]) than in front-to-back clusters. The proposed explanation for this effect is perceptually based: Starting the C2 closure of a back-to-front cluster too soon will hide the acoustic information needed for the listener to identify C1, whereas this is not true for front-to-back clusters. Results from other studies (Chitoran & Goldstein, 2006; Kühnert, Hoole, & Mooshammer, 2006; Zeroual, Hoole, Gafos, & Esling, 2014) have called into question the generality and/or the perceptual motivation of the place order effect. To illustrate, Gafos et al. (2010) analyzed only stop-stop clusters within each word position and speaker, and found that Speaker 2 showed significant place order effects consistent with findings in other studies: Front-to-back stop-stop clusters were significantly more overlapped than back-to-front clusters word-initially and word-medially. There was no significant difference word-finally. The overlap patterns for Speaker 1 were significantly different both from what had been reported in other studies and from Speaker 2. In particular, contrary to the expected place order effect, Speaker 1 had significantly more overlap for back-to-front clusters, both as a main effect (across all word positions) and word-initially. Gafos et al. (2010) concluded that this was related to the fact that Speaker 1 had less cluster overlap than Speaker 2. Such results along with questions about the validity of an unqualified or universal place order effect in previous studies across languages and speakers led them to propose that place order effects are in fact relativized: If there are two environments (say, two different speakers or two different phonetic contexts) in which place order effects may arise and they arise in only one, the place order effect will be found in the environment where there is more overlap. The reasoning behind the hypothesis is that it is only in the more overlapped environment where recoverability is at stake and thus the effect should emerge; in a less overlapped environment, recoverability of acoustic information is less threatened and therefore the place order effects on overlap need not come into play.

Lastly, the amount of overlap in clusters may also be to some degree idiosyncratic to individual speakers. Gafos et al. (2010) showed that there was a significant difference between speakers in the amount of overlap in stop-stop clusters, with Speaker 1 having less overlapped stop-stop clusters than Speaker 2. This held overall and within each word position.

The above influences on overlap were therefore taken into consideration in evaluating the hypotheses tested in the present study, both in the selection of stimuli and in the design of the statistical models (see the Methods section for further details).

Verifying the validity of H-J2, H-3_PV, or H-3_stiffness would be useful in at least two ways. First, these hypotheses make predictions about overlap in cases where other hypotheses in the literature about overlap make no predictions. Comparing, for example, word-medial [ɡd] and [ɡb] clusters: Neither the word position nor the place order hypotheses make any predictions about the expected amount of articulatory overlap since the clusters are in the same word position and are both back-to-front. However, H-J2 predicts more overlap for [ɡb] than for [ɡd]. The two formulations of H-3 would also make predictions about these overlap in these clusters, not based on the articulator, but rather on the relative peak velocity or relative stiffness of the closing movements of C1 and C2.

Second, overlap is a continuum regardless of whether measure (3) or (4) is used, and there are multiple influences on the amount of overlap that were observed in any given cluster. The hypotheses tested here may possibly account for cases where observed articulatory overlap runs contra these other (word position and place order) hypotheses. For example, if [bd] clusters are observed to have less overlap than [db] clusters, this is not expected based on the place order hypothesis, but it could be accounted for by one of the present hypotheses. This could potentially explain the nature of the distributions of overlap more adequately, rather than unpredicted cases being viewed as exceptions or arising from noise.

Six native speakers of the Oujda dialect of Moroccan Arabic (spoken in Northeast Morocco, near the Algerian-Moroccan border) were recorded. The speakers (one female, five male) ranged in age from 25 to 38. All speakers provided written informed consent to take part in the experiment, and were paid for their participation in the experiment. The experimental procedures were approved by the Ethics Committee of Ludwig-Maximilians-Universität.

The movement of speech articulators was tracked with 3D EMA at 200 Hz sampling rate. Concurrent audio recordings sampled at 24 kHz were made. Recordings were made at the EMA lab in the Institut für Phonetik und Sprachliche Kommunikation (now the Institut für Phonetik und Sprachverarbeitung), Ludwig-Maximilians-Universität München, Germany. EMA receivers included for analysis in the current study were attached to the speaker’s lower lip, tongue tip, and tongue body. EMA receivers were also affixed at the nasion, right and left mastoid, and upper incisor to allow for head correction of the movement of the sensors used in analyses. The data were processed following Hoole and Zierdt (2010).

There are two types of consonant sequences types in MA: clusters with no inter-consonantal vowel (CC, e.g., [kbaʃ]) and sequences of two consonants where an optional schwa-like vocoid can appear between the two consonants (CˆC, e.g., [kˆbda]). The precise nature of this vocoid and the phonology that accounts for it is a matter of some debate (cf. Dell & Elmedlaoui, 2002; Gafos et al., 2010; Heath, 1987). CˆC sequence types were therefore not included in the present study: Only unambiguous CC clusters were analyzed. Another consideration in selecting stimuli for the present experiment is that Gafos et al. (2010) found that word-medial clusters were significantly more overlapped than word-initial and word-final clusters. The same study also found that the place-order effect on overlap was more likely to be found in contexts where clusters are otherwise more overlapped than in those where they are less so. If the same is true for the effects of differences in peak velocity and/or measured stiffness of the consonants in a cluster, these effects should most likely be observed in word-medial clusters. Therefore, all stimuli were real words containing two-consonant, word-medial clusters, where both the preceding and following vowel were [a]. Restricting the stimuli to word-medial clusters allowed us to control for word position rather than increase the complexity of our analyses by including another predictor.

The data analyzed in the present study are a subset of articulatory recordings from three corpora that were collected for other experiments. The stimuli are shown in Table 2, broken down by corpus. The stimuli in corpus 1 were produced by Speaker 1. Those in corpus 2 were produced by Speakers 2 and 3. Those in corpus 3 were produced by Speakers 4, 5, and 6. Stimuli were presented on a computer screen in Arabic script, including diacritics indicating full vowels, within a carrier phrase. Articulatory and acoustic recordings were made as the speakers read these stimuli from the computer screen. The presentation order of all stimuli was pseudo-randomized for each speaker. All speakers produced each of their stimuli five times, such that there were always other stimuli intervening between any two productions of a given stimulus.

First we tested H-J1, which posits that there should be inherent differences in the rapidity of articulations based on the primary oral articulator, with the tongue tip being fastest, lower lip being intermediary, and tongue body being slowest. Figure 4A shows the data relevant for H-J1 using peak velocity, and Figure 4B shows the data relevant for H-J1 using measured stiffness. The distributions are broken out by cluster position (C1 or C2) to take into consideration the potential effect of position on rapidity, and because the patterns of assimilation discussed by Jun (2004) pertain to cluster positions separately, as shown in Figure 1 (A-B versus C-D).

The peak velocity of the closing movements showed very little difference by articulator when in C1 position, though numerically they did pattern as predicted by H-J1. Peak velocities were slower in general for C2 consonants, especially for lower lip movements, which were slower not just compared to lower lip peak velocities in C1, but compared to the other two articulators in C2. This pattern is inconsistent with the prediction of H-J1. The numerical pattern for measured stiffness was consistent with the prediction of H-J1, and as with the peak velocities, stiffness values were lower in C2 position than in C1.

Two linear mixed-effects models (Baayen, Davidson, & Bates, 2008; Gelman & Hill, 2007) were fit to test whether the differences shown in Figure 4 were significant, using the LME4 package (Bates, 2005; Bates et al., 2020) for R (R Development Core Team, 2018). Speaker and stimulus were modeled as random effects, with one model having peak velocity as its predicted value and the second for measured stiffness. The fixed effects in the models were articulator and cluster position (C1 or C2), as well as the interaction between the two. Each model included a random slope for articulator by speaker. Omnibus test statistics for the fixed effects (articulator, cluster position, and their interaction) were determined using a type III analysis of variance with Satterthwaite’s method from the ANOVA function of the STATS package (R Development Core Team, 2018) in R. Post-hoc differences in articulator-cluster position combinations were assessed using estimated marginal means (Searle, Speed, & Milliken, 1980) using the EMMEANS package (Lenth, Singmann, Love, Buerkner, & Herve, 2019) for R. Degrees of freedom were calculated using the Kenward-Roger method. The R script used to fit these and all of the mixed-effects models, as well as to generate all of the figures, in the present study are available in the file indicated in Supplementary Material 2.

Results of the ANOVA are shown in Table 4. There were no significant differences in peak velocity based on articulator, though cluster position and the interaction were significant. For measured stiffness, all three fixed effects were significant.

Table 5 shows the results of the comparisons of all articulator-cluster position combinations, providing more insight into the results of the ANOVA shown in Table 4. Pairs with a Tukey-adjusted p value < 0.05 were deemed reliable and are shown in bold. For peak velocity, there were no reliable differences based on articulator in C1 position. In C2 position, the only reliable difference between articulators was that the lower lip had lower peak velocity than the tongue body. The peak velocity of the lower lip was significantly higher in C1 position than in C2, but there was no difference based on cluster position for the other two articulators. The only other significant difference was that the lower lip in C2 position had lower peak velocity than the tongue body in C1. There was no support for H-J1 based on peak velocity.

In C1 position, the tongue body had significantly lower measured stiffness values than either the tongue tip or lower lip. There were no significant differences between any articulators in C2 position. Similar to peak velocity, the lower lip had significantly lower stiffness values in C2 position than in C1. The tongue body also had lower measured stiffness than the other two articulators when it was in C2 position and the other articulators were in C1. The tongue body in C1 position also had significantly lower stiffness than the tongue tip in C2 position. There was therefore limited support for H-J1 based on the measured stiffness of the articulators: The tongue body had lower stiffness than the other two articulators, but only within C1 position or sometimes across cluster positions. Interpreting the cross-position differences is complicated by the main effect of Cluster Position (with C2 having lower stiffness values). The significant effect of articulator in the ANOVA was therefore due to these differences between the tongue body and the other articulators, given that the predicted difference in measured stiffness between the tongue tip and lower lip was found neither within nor across cluster positions.

In this section we test hypothesis H-J2. We have noted already that H-J2 is predicated on H-J1, and we have just shown that there is limited support for H-J1 in these data. Nevertheless, there is still a possibility that articulator-specific differences may be evident only when consonants are analyzed using measures that are within-token. Looking at within-token overlap within cluster types is the most direct test of hypothesis H-J2. Figure 5A and B show the Relative Overlap and Onset Lag, respectively, with the cluster types organized such that the amount of overlap should decrease from left to right, according to hypothesis H-J2.

The patterns of overlap do not correspond to those expected based on H-2J, regardless of the index of overlap that was used. Most notably, the two tongue tip-initial cluster types have much less overlap than the two lower lip-initial cluster types, comparable to the amount of overlap with the two tongue body-initial cluster types, which were expected to have the least overlap. Apart from the tongue tip-initial clusters, overlap by cluster type patterns more or less as expected.

The reliability of the differences shown in Figure 5 was evaluated with two linear mixed effects models in which cluster type was the fixed predictor (with six levels), one of Relative Overlap and another in which cluster type was the fixed predictor of Onset Lag. Random intercepts were included for speaker and stimulus. Full pairwise comparisons of all cluster types would require statistical power beyond what is available in our data. Therefore, we set the cluster type tongue tip-tongue body as the reference level, since this cluster type was predicted to have the highest overlap according to H-J2 yet had numerically lower overlap than lower lip-initial clusters. Structuring the model this way allowed us to determine whether tongue tip-tongue body clusters had reliably more overlap than tongue tip-lower lip clusters (per H-J2), whether the greater overlap for the two lower lip-initial clusters was reliable (contra H-J2), and whether the tongue body-initial clusters had less overlap than the tongue tip-tongue body clusters (per H-J2). Place Order could not be included as a predictor because it is too correlated with cluster type.

Table 6 shows that there was no reliable difference in overlap between tongue tip-tongue body and tongue tip-lower lip clusters (not supporting H-J2), and that the overlap for the two lower lip-initial clusters was reliably greater than for the tongue tip-tongue body clusters (contra H-J2), regardless of whether overlap was indexed by Relative Overlap or Onset Lag. Both tongue body-initial clusters had less overlap than the tongue tip-tongue body clusters (per H-J2), but only when overlap was indexed by Onset Lag. There was no reliable difference when indexed by Relative Overlap.

Lastly, we tested H-3_PV and H-3_stiffness, using both Relative Overlap and Onset Lag as indexes of overlap. The relationship between PV Difference and Stiffness Difference, and Relative Overlap and Onset Lag is shown in Figure 6. The black lines are linear regression lines across all cluster types. The colored lines are linear regression lines within cluster type.

Four linear effects models were fit to the data, one for each relationship shown in Figure 6. Speaker and stimulus were modeled as random effects. The fixed effects were Peak Velocity Difference or Stiffness Difference (both continuous), and Place Order (categorical). Cluster type was not included as a predictor, as the models would not converge if it was added. Since Gafos et al. (2010) found Place Order effects were speaker-dependent for a subset of the speakers included in the present study, speaker-specific random slopes for Place Order were also included in the model. The results of all four models are shown in Table 7.

Results of the statistical models confirm that Stiffness Difference was a significant predictor of overlap, regardless of whether Relative Overlap or Onset Lag was used to index overlap, with overlap increasing as Stiffness Difference increased. These results provide strong support for hypothesis H-3_stiffness. On the other hand, no support was found for hypothesis H-3_PV: Peak Velocity Difference was not a significant predictor of overlap, regardless of whether Relative Overlap or Onset Lag is used to index overlap. Place Order was not a significant predictor of overlap other than in the model that included Peak Velocity Difference as a predictor of Onset Lag (Figure 6B/Table 5-B), where it predicted greater overlap in front-to-back clusters, as expected.