Based on the interpretation of the observed Georgian production patterns, we test the following hypotheses:

In the present study, we test the hypotheses in two perception experiments with native listeners of Georgian. The perceptual responses are then evaluated against detailed acoustic and articulatory (EMA) analyses of the Georgian stimuli.

Experiment 1 is a forced choice identification task, in which listeners heard CCV portions excised from words containing stop-stop sequences. They were asked to decide whether the short sound they heard began with a CV or a CC sequence. It is predicted that listeners would more accurately identify stimuli as beginning with CC when timing lag is longer, and thus perceptual recoverability is assumed to be enhanced. The best CC identification rates are therefore expected for target stimuli based on initial back-to-front sequences (e.g., #gdV), which are the least overlapped (have the longest lag), and the poorest rates are expected for stimuli containing word-medial front-to-back sequences (e.g., dgV), which are the most overlapped (have the shortest lag).

Experiment 2 was conducted a week later, with the same participants. It consisted of a transcription task. Each listener was exposed only to those stimuli for which they previously gave a CV answer and asked to transcribe the short sequence by hand. This transcription task determined whether listeners responded CV in Experiment 1 because they failed to detect C1 in the stop-stop sequence, or because they detected a vocalic portion between C1 and C2, and treated it as a vowel. Experiment 2 thus complements Experiment 1 by allowing for reliable answers to each of the two questions of the study:

The stimuli were prepared based on the acoustic and articulatory Georgian data previously collected at Haskins Laboratories, in New Haven, CT, and analysed in Chitoran et al., (2002). One specific speaker was selected to provide the stimuli, based on the fact that he produced more frequent vocalic transitions between C1 and C2 in his speech. The speaker, a male in his mid 20s, had been living in the United States for approximately two years at the time of the recording and reported using Georgian on a regular daily basis. He reported no speech or hearing impairment. Prior to the start of the experiment, the speaker was informed of the purpose of the study; he read and signed consent forms (all of this was done in English).

Simultaneous acoustic and kinematic data were collected for stimuli, including stop-stop sequences and filler items (Table 1). Target stimuli were recorded along with other distractors, which included CC combinations other than stop-stop. The stop-stop sequences in this study are identical tokens to the ones whose production was analyzed in Chitoran et al. (2002). Each target word was produced in the carrier phrase [sit’q’wa ____ gamoitʰkʰmis orʤer] (‘The word ____ is pronounced twice’). A computer screen presented the sentences one at a time, in Georgian script. The speaker was invited to read each sentence aloud at a normal pace. If he paused or had a false start, he was asked to re-read the entire sentence. Fourteen repetitions of each sentence containing a stop-stop target word were presented in randomized order and recorded.

Intervocalic multiple consonant sequences in Georgian are reported to be syllabified as a simplex coda followed by a complex onset (Harris, 2002). However, when the sequence consists of only two consonants, the syllabification intuitions of native speakers vary, oscillating between a complex onset (V.CCV) and a coda+onset (VC.CV).

The EMA magnetometer system (Perkell et al., 1992), in use at Haskins at the time, was used for data collection. Two receiver coils were attached to two midsagittal points on the tongue: approximately 1 cm from the tongue tip (TT), and tongue dorsum (TD) as far back as possible, to capture velar constrictions. One coil each was placed on the upper and lower lip on the midsagittal plane. Reference coils were placed on the upper and lower teeth and on the nose bridge, and were used for head movement correction.

From the recorded C1C2 sequences, six different stop-stop sequences – /bg, dg, pt, gb, gd, tb/ – varying in their place order (three front to back F-B, three back to front B-F) were selected as the target stimuli. Other C1C2 sequences that are not the target six sequences were used as fillers, in order to vary the types of stimuli to which the listeners were exposed. Multiple productions (three to five) were included for each sequence in order to examine the effect of timing variation naturally present in Georgian CC production. For this speaker, one /gatʰba/ and two /tʰbeba/ tokens were lost due to mispronunciation.

Some of the selected productions included C1 vocalic releases, which we indicate henceforth with a superscript ^V: C1^VC2. The selected stimuli were excised C1C2V portions (Figure 2), segmented on the acoustic signal from the midpoint of C1 closure to the midpoint of V, adjusted to the nearest zero-crossing, and avoiding coarticulation with the following consonant.

Four C1VC2V sequences were included as controls: [deba], [geba], [bile], [t’ebi]. These were the last two syllables excised from the trisyllabic words [agdeba], [adgeba], [sat’k’bile], [pʰakʰt’ebi], from the midpoint of the C1 closure to the midpoint of the final vowel. In these four Georgian words, the initial syllable is stressed. By removing it, we made sure there was no prominence on the first vowel in the remaining CVCV sequences. All target stimuli and controls were attested word onsets in Georgian. They were segmented and normalized for intensity using Praat (Boersma & Weenink, 2021).

The perception experiment was conducted in Tbilisi, Georgia, in a quiet office in the Linguistics Department at Tbilisi State University. Twenty-nine Georgian native listeners (17 female) participated in two experiments. A Georgian student research assistant was hired to recruit participants; these were students at Tbilisi State University, recruited via fliers and the assistant’s personal contacts. The assistant explained to the participants, in Georgian, the information letter, the consent forms, and the experiment instructions.

In Experiment 1, the forced choice identification task, a total of 222 stimuli were presented to, and randomized across, each participant. These included 114 target stimuli, 70 fillers, and 38 CVCV controls. After hearing each stimulus, the listeners were asked to identify whether it begins with a sequence of two consonants ‘cc’, or with a consonant-vowel sequence ‘cv’. Before the experiment, each participant confirmed that they were familiar with the terms ‘consonant’ and ‘vowel’, and were able to give accurate examples of each.

Experiment 2 was conducted a week later with the same participants, to disambiguate the ‘cv’ response in Experiment 1. In Experiment 1, listeners could have responded ‘cv’ for a stimulus such as /bge/, for example, if they heard a vowel between the stops [b^vge], but also if they heard only one of the stops [_ge] or [b_e]. Experiment 2 thus consisted of a transcription test, in which listeners heard subsets of the stimuli from Experiment 1. Each participant heard all tokens (three to five) of any stimulus item for which they had given at least one ‘cv’ response in Experiment 1, and was asked to transcribe what they heard by hand in Georgian orthography. This ensured that multiple productions of the same word were included when applicable. Georgian orthography being phonemic, IPA transcription of the responses is relatively easy. It was done by the first author, who is familiar with the Georgian alphabet. The native Georgian research assistant also verified the participants’ transcriptions.

Both experiments were conducted on a Windows laptop computer, using a program written and kindly provided by René Carré and Emmanuel Ferragne. Both experiments were self-paced, and each one lasted between 20 and 25 minutes. For Experiment 1, the forced choice identification task, participants were seated in front of the computer, and the stimuli were played back via headphones, one at a time. They were asked to listen to each sound and respond by pressing either the F or the J key on the keyboard. The F key was labelled as ‘cv’, and the J key as ‘cc’, using Georgian letters: თხ [tʰx] for ‘cv’, თანხმოვანი – ხმოვანი [tʰanxmovani–xmovani] ‘consonant–vowel’, and თთ [tʰtʰ] for ‘cc’, თანხმოვანი – თანხმოვანი [tʰanxmovani–tʰanxmovani] ‘consonant–consonant’. After each response, the participants clicked on the screen to move on to the next stimulus.

Experiment 1 was preceded by two practice blocks. In the first block, participants were asked to simply listen to 10 stimuli, to familiarize themselves with the short sounds they would hear. In the second practice block, they listened and responded to 10 different stimuli. This was to make the participants familiar with the task, and thus no feedback was provided for the practice trials. The 20 practice stimuli were not included in the actual experiments.

We would like to acknowledge that using perception stimuli recorded with EMA might raise concerns about possible speech distortions and intelligibility due to the EMA sensors. To the best of our knowledge, the effects of EMA sensors on the perception of speech are largely unexplored. Several studies have tested the effect of sensors on production, comparing typical and disordered speech such as aphasia, apraxia, and Parkinson’s Disease (e.g., Katz et al., 2006; Tienkamp et al., 2024). These studies reported some interference in the production of sibilant fricatives and in the acoustic-articulatory vowel space. While we cannot rule out the possibility that the presence of EMA sensors may have interfered to some extent with the Georgian speaker’s production, our stimuli come from a single speaker and were carefully selected based on the articulatory measure of overlap, on which our hypotheses are crucially based. Since testing an articulatory hypothesis is the main goal of our study, we prioritized this latter point. We were ultimately reassured by the relatively high accuracy scores of the transcriptions obtained in Experiment 2: C1 was correctly transcribed 66% of the time and C2, 75% of the time. These scores do not reflect serious intelligibility issues. Moreover, the higher accuracy of C2 transcriptions is what we expected to find, given that a vowel always followed C2. This suggests that sensor interference, if any, was limited.

To understand the relation between the participants’ responses and the phonetic properties of the stimuli, we analyzed the acoustic and articulatory properties related to the timing lag (or overlap) between C1 and C2, as well as those related to the vocalic release of the C1. The following three acoustic parameters were measured:

Three articulatory measures were examined, based on the articulatory landmarks measured on the EMA signal:

In EMA signals, movement trajectories of the receiver coils attached to the tongue tip, tongue dorsum, upper lip, and lower lip were evaluated. The articulatory constriction formation and release were identified using the Matlab analysis program, Mview (provided by Mark Tiede). The Mview algorithms allow the computation of articulatory landmarks based on the velocity profiles of the relevant sensors. The peak velocities of the constriction formation and release movements were calculated algorithmically. For each gesture, the following three points were identified and labelled, using a 20% threshold of the velocity peaks: the gesture onset, constriction (target) achievement, and constriction release. For labials, the Euclidean distance between upper and lower lip receiver coils was used to compute lip aperture, and thus measure labial constrictions. For coronals, the gestural landmarks were determined by evaluating the distance of the tongue tip receiver coil to the closest point on the palate. For dorsals, the vertical position of the tongue dorsum coil was used.

The relative overlap measure in (6), like the overlap measure in Chitoran et al. (2002), essentially quantifies what proportion of the C1 plateau (from achievement to release) is free from the influence of the C2 gesture (see Figure 4). However, as the relative overlap measure is calculated by subtracting the overlap measure in Chitoran et al. from one, the current measure straightforwardly corresponds to the degree of relative overlap: Greater values thus correspond to the greater degrees of overlap, or the greater influence of C2 movement on C1.

As expected, some measures are correlated. When all stimuli were considered (Table 2), all measures except for the acoustic lag and the release lag showed significant correlations in the expected direction (i.e., positive correlations among the lag measures and negative correlations between the lag measures and the overlap measure). When considering the stimuli that had vocalic releases (Table 3), the duration of the vocalic release does not correlate significantly with any of the lag or overlap measure.

We also tested whether the presence of a vocalic release was associated with gestural timing and overlap measures using point-biserial correlations. None of the correlations reached statistical significance: acoustic lag [r = .20, p = .13], onset lag [r = .17, p = .19], release lag [r = –.11, p = .41], and relative overlap [r = .18, p = .18]. These results indicate that the presence or absence of a vocalic release does not reliably co-vary with timing lag or gestural overlap.

To evaluate H1, we considered four measures: acoustic lag, onset lag, release lag, and relative overlap. They are not independent from one another (see Tables 2 and 3), and thus we built a separate series of mixed effect logistic regression models for each measure, using the lme4 package (Bates et al., 2015) in R (R Core Team 2022). The models predicted the likelihood of correct C1 identification (binary outcome, correct vs. incorrect), based on the fixed effects of Place Order (B-F vs. F-B) and C1 Voicing (voiceless vs. voiced), along with one of the four measures under consideration. For random effects, by-subject and by-item intercepts, as well as all possible random slopes, were considered. We began by building the full model with all possible interaction terms and the fullest random effects structure, and then found the optimal model by removing the interaction terms and the random slopes that do not contribute to the model fit. Nested models were compared using likelihood ratio tests, and AIC/BIC values were examined to confirm consistency across model comparisons. In all comparisons, AIC, BIC, and likelihood ratio results were consistent.

As the timing measures are expected to co-vary with the place order, at least to some extent, we evaluated the multicollinearity with VIF (variation inflation factors) and removed the predictors that are highly correlated with the measure of interest (indicated by VIF values greater than 5, e.g., Shrestha, 2020). This was to build the most reliable model that can best predict the C1 identification using the measure under consideration (i.e., other predictors were less important than the measure of interest). Table 4 summarizes the best models for each of the four measures examined.

To determine whether each of the measures contributes significantly to C1 identification, the best models in Table 4 were compared with the model without the measure in question, using the likelihood ratio tests. If inclusion of the measure improves the model fit, we concluded the measure facilitates C1 identification.

Each of the four measures significantly contributed to the model fit, supporting H1. First, C1 identification was influenced by Acoustic Lag [χ²(1) = 15.02, p = 0.0001***], Onset Lag [χ²(1) = 15.30, p < 0.0001***], and Relative Overlap [χ²(1) = 20.04, p = 0.0001***]. The likelihood of correct C1 identification increased, as Acoustic Lag [β = 0.025, z = 3.91, p < 0.0001***] and Onset Lag [β = 0.027, z = 3.97, p < 0.0001***] increased and Relative Overlap [β = –0.393, z = 4.70, p < 0.0001***] decreased, as expected. As shown in figures 5, 6, and 7, these effects were consistent across Place Order or C1 Voicing.

On the other hand, the best model for Release Lag included a significant interaction between the measure of interest (release lag) and place order [x²(1) = 14.19, p = 0.0002***]. To understand the significant interaction better, post-hoc analyses were conducted using emtrends() in the emmeans package (Lenth, 2022). The post-hoc analyses revealed that the influence of Release Lag differed significantly in back-to-front and front-to-back sequences [z = 3.90, p = 0.0001***]: Longer release lags led to a greater chance of correct C1 identification in back-to-front sequences (slope = 0.014, [CI: 0.007, 0.021]), but not in front-to-back sequences (slope = –0.004, [CI: –0.011, 0.003]), as shown in Figure 8.

To test whether the vocalic release (transitional vocoid) helps the recovery of C1 gesture, we tested (1) whether the tokens with vocalic releases (Presence of Vocalic Release) were better identified, and (2) for the tokens with the vocalic releases, whether the releases of longer duration (Duration of Vocalic Release) led to better identification. The statistical models followed the same structure as in Section 6.1, except that C1 voicing was not considered, as none of the tokens with voiceless C1 included vocalic releases. Also, the vocalic release duration model was fitted to the subset of the data which contains only the stimuli produced with vocalic releases (n = 2,101, among which 1,148 was back-to-front. The entire dataset included 3,107 observations). Table 5 shows the best models for the two measures.

First, the presence of a vocalic release influenced the C1 identification, but the effect interacted with Place Order [x²(1) = 18.01, p < 0.0001***], as shown in Figure 9. A post-hoc pairwise comparison on the significant interaction revealed that, in back-to-front sequences only, C1 in the tokens without vocalic releases was better identified than C1 in those tokens with vocalic releases [β = 2.089, z = 3.97, p = 0.0001***]. For front-to-back sequences, presence versus absence of vocalic releases did not influence C1 identification [p = 0.55]. Overall, the presence of a vocalic release did not improve C1 identification.

For the tokens produced with the vocalic releases, the duration of these releases was a significant predictor of C1 identification regardless of the Place Order [x²(1) = 10.73, p = 0.0011**]. However, the duration of vocalic releases and the C1 identification were inversely related as shown in Figure 10. That is, as the duration of the releases increased, the identification of C1 became worse [β = –0.056, z = –3.41, p = 0.0007***]. Participants inaccurately transcribed C1 when the releases were longer.

In sum, the outcomes of the two models related to the vocalic release clearly show that the vocalic releases do not facilitate the correct identification of C1. Rather, such transitional vocoids may interfere with the identification of the intended C1, especially when they are longer.

The current findings support the first hypothesis that longer timing lag and reduced overlap between C1 and C2 facilitate the recovery of C1 gestures: Georgian native listeners do benefit from longer lag between C1 and C2 in recovering C1 in stop-stop sequences. Among the four measures tested, Acoustic Lag, Onset Lag, and Relative Overlap, each contributed to increasing accurate C1 identification, as predicted. Among these, Acoustic Lag and Onset Lag are moderately correlated.

The fourth lag measure we considered—Release Lag—does not correlate with Acoustic Lag and shows only a weak correlation with Onset Lag. Our results showed an interaction between Release Lag and place order, indicating that the influence of release lag differs significantly in B-F and F-B sequences. In B-F sequences, C1 accuracy did improve with longer release lags, but this was not the case for F-B sequences.

Overall, the results concerning timing lag are consistent with H1. Georgian native listeners identified C1 more accurately in sequences with reduced overlap, when the gesture for C2 is timed later relative to C1. This effect was evident in the timing of the C2 gesture onset for all the stop-stop sequences considered. The later timing of the C2 release proved beneficial for the B-F sequences (e.g., /gb/, /tʰb/), presumably because the posterior-to-anterior transition increases the chance that a C1 release is masked. Release Lag had no significant impact on F-B sequences, suggesting that the perceptual vulnerability of C1 may vary by place order. These results thus align with the production patterns observed in previous studies (e.g., Chitoran et al., 2002), suggesting that native speakers may draw on perceptual knowledge when timing consecutive constrictions in onset clusters. The current findings, taken together with the previous findings on production patterns, provide converging evidence that timing lags (especially those based on gestural onsets) play a considerable role in recovering consonantal gestures. This suggests that the inter-consonantal timing lags within onset clusters are not merely a phonetic artefact but a part of grammar functionally encoded by Georgian speakers.

It is worth mentioning at this point that the overlap measures we considered crucially rely on gestural releases. This includes the acoustic measure we used—the inter-burst interval. Is it possible, though, that listeners may use other types of acoustic information that would correspond to consonant overlap? One possibility we have not considered, but has been proposed by Zsiga (2003), is duration ratio, a relative measure defined as the mean closure duration of the C1C2 cluster (with or without an intervening release) divided by the sum of the mean closure durations of single, intervocalic C1 and C2. This has been a useful measure of overlap across word boundaries in comparing non-native productions (English speakers producing L2 Russian utterances vs. Russian speakers producing L2 English utterances). It is worth verifying in the future whether this relative acoustic measure may be better correlated with the articulatory overlap measures.

For now, however, since C-C coordination with reduced overlap is known to often result in a vocalic release (transitional vocoid), especially in voiced sequences, we turn to the question of whether the presence of such vocalic releases benefits the recoverability of C1.

The second hypothesis, that the presence of C1 vocalic releases with richer C1 formant transition information would help listeners recover C1, is not supported by our results. Vocalic releases instead seem to be detrimental. This suggests that, unlike timing lag or degree of overlap, the presence of vocalic releases between two stops in C1C2 sequences may not be related to perceptual considerations. Or, if native speakers of Georgian indeed produce them for the listeners’ sake, this result suggests that their efforts are ineffective—at least in the stop-stop sequences examined in this study.

An interaction with Place order was present in the results related to vocalic releases, as well. In F-B stop-stop sequences, the presence of a vocalic release had no effect on C1 identification. In B-F sequences, contrary to expectations, tokens without a vocalic release were more accurately identified than those with a vocalic release, a result which is not consistent with H2.

The duration of the vocalic release, when present, emerged as a significant predictor of accuracy, but again, contrary to H2, as the vocoid duration increases, C1 identification actually decreases. The inverse correlation between vocalic release duration and perceptual accuracy of C1 is at odds with findings from previous cross-language studies. For example, Wilson et al. (2014) and Wilson and Davidson (2013) found that longer burst duration resulted in more epenthesis between C1 and C2 stops when shadowing foreign speech, proposing that a longer burst has a similar acoustic profile to that of a vowel. In their study, greater burst amplitude similarly increased the rate of epenthesis. Both longer burst duration and greater burst amplitude are interpretable as presence of a vocoid, which protects C1 from misperception. However, these were the results of non-native listeners (English listeners hearing Russian stimuli), whose native phonotactics led them to interpret the acoustic properties of a longer and louder burst as a vowel. Georgian listeners confronted with non-native French data (Kwon & Chitoran, 2024) behaved differently because of their different native phonotactic expectation. Used to the presence of a vocalic release in a CC sequence, they did not reliably distinguish French CCV vs. CVCV stimuli in a non-native AX discrimination task. This result and the current findings together suggest that the quality and duration of V1 relative to V2 matters. When V1 is a vocalic release (transitional vocoid) and significantly shorter than V2, a lexical vowel, Georgian listeners tend to ignore it. However, a longer vocoid, one comparable in duration with the lexical vowel but not in its spectral properties, has a negative effect on C1 identification. Georgian listeners are thus sensitive to a longer vocoid, but it provides them with misleading information.

A possible reason for this particular result may be that the presence of a long vocalic release interferes with the perception and recoverability of information at multiple structural levels, not just the segmental level. It can simultaneously affect the perceptibility of C1, as well as the perceptibility of higher-level prosodic structures, such as syllables or words, thus impinging on overall intelligibility. A vocalic release may be perceived as a full vowel, therefore a syllable nucleus. In support of this interpretation, Crouch et al. (2023b) found that transitional vocoids in Georgian C1C2 sequences alter the amplitude envelope in ways that may lead to resyllabification. In sequences with sonority plateaus or falls, productions with a vocoid showed an additional peak in the amplitude envelope, compared to productions of the same sequence without a vocoid. This additional peak can be interpreted as a syllable nucleus and may lead listeners to mis-parse syllable boundaries, particularly when the vocoid is long enough to be perceptually salient but not clearly vowel-like. This suggests that speakers may avoid vocoids in certain cluster types not to preserve segmental clarity but to avoid prosodic ambiguity. If so, then phonological grammar may encode not just segmental recoverability but also the recoverability of the syllabic organization. These findings are relevant for our current study, in which longer vocalic releases led to more CV responses and proved not effective for C1 identification.

So far, we (and other authors before us) have only considered segment-level perceptibility, with a focus on the recoverability of phonological contrasts. But we must consider the possibility that patterns that are predicted to facilitate the recovery of segmental contrasts may not be equally beneficial, and may even hinder, the recovery of syllable- or higher-level information.

We now return to our two initial hypotheses to consider what we have learned. Do the observed patterns—timing lag and vocalic releases—truly present an advantage for the recoverability of phonological information? Based on our results, we can maintain that the observed lag and overlap patterns in stop-stop sequences indeed help recover the phonological identity of C1, supporting H1. Vocalic releases, however, do not, contra H2. This is explained by the finding that the presence of the vocalic release is not linearly correlated with lag and thus does not provide a reliable cue to temporal organization. It may be used, instead, for conveying other types of information, such as voicing or cluster type.

Unlike prior studies that inferred timing-based perceptual effects from production data or nonnative perception data, the current findings offer direct perceptual evidence from native listeners. We provide evidence that timing lag matters in speech perception, contributing to segmental recovery in consonant clusters. These findings suggest that the native listeners rely on timing lag as the phonetic details relevant for recovering consonantal gestures. Yet the lack of perceptual benefit from vocalic releases suggests that not all phonetic patterns assumed to enhance recoverability serve that function. This contrast underscores the importance of evaluating perceptual recoverability empirically, rather than assuming their perceptual efficacy based on production patterns alone.

Corroborating previous findings on Georgian productions (Chitoran et al., 2002; Crouch, 2022), the current results support the inclusion of timing lag in the grammar of Georgian. Listeners benefit from longer lag (i.e., reduced overlap) and speakers tend to produce longer lag when C1 is expected to be more perceptually vulnerable, though it remains unclear whether speakers are manipulating the lag to aid listeners.

Going back to Mattingly (1981), we may ask: Do the speakers’ phonological grammar integrate knowledge about temporal patterns and their acoustic consequences? The different timing lags in back-to-front and front-to-back sequences observed in production may be perceptually motivated in stop-stop sequences and learned as grammatical generalizations. Additional factors, not related to perceptibility, may also contribute to timing patterns. Pouplier et al.’s (2022) comparison of lag patterns in the same CC clusters across seven languages firmly highlighted wide language-specific diversity, and, at the same time, consistent lag patterns across languages in terms of the segmental composition of the clusters. Among the seven languages, Georgian stands out as having the largest variability of lag durations. It is the language that reaches the longest lag durations, but in other respects it also conforms to cross-linguistic cluster-specific patterns. For example, the Georgian /sC/ clusters align with those of the six other languages in having the shortest lag. The large within-language variability of lag in Georgian is consistent with our conclusion that timing lag is part of speakers’ knowledge. Controlling lag allows the recovery of many types of onset clusters, some of which, importantly, contain morphological information. Georgian is a language with rich prefixal morphology, where multiple consonantal prefixes may be added. This particular aspect of the language structure highlights the importance of both segmental and prosodic recoverability. A consonantal prefix must be accurately recovered and, at the same time, adding consonantal prefixes must not alter the syllable count. The precise control of inter-consonantal timing lag, avoiding long vocalic releases, is therefore important for these closely intertwined intelligibility considerations.

The current findings suggest that perceptual recoverability should not be defined strictly at a local, segmental level. A more fruitful approach should consider multiple levels of structures simultaneously—segments, syllabic organization, morphological structure—in the context of the overall intelligibility of the message. Speakers likely aim to be understood and to accommodate the listener, but this intent may be reflected in the phonological grammar in a complex way.

A further important point that needs to be raised is whether diachronic information should also be considered when examining timing patterns. Easterday (2019) raised this issue specifically regarding obstruent sequences in languages classified as having a highly complex syllable structure. She notes that the most common typological source of clusters is vowel reduction, and some characteristics of the reduced vowel may be retained in C1 release bursts. From a diachronic perspective then, the observed place effects may be seen as motivated by perceptual recoverability or a consequence of perceptual recoverability. The timing patterns and perceptual properties of such clusters may have the effect of preserving complex syllable structure by protecting it from complete overlap that can lead to consonant loss. Georgian stop-stop sequences are known to have two historical sources (Gamkrelidze & Ivanov, 1995). Back-to-front sequences developed through the deletion of an intervening vowel. In line with Easterday’s reasoning, it can be argued that B-F sequences may have preserved the timing of a lost vowel. Front-to-back sequences with a dorsal C2 are known as ‘harmonic clusters’, and have developed from velarized stops, single segments that subsequently broke into sequences (e.g., [dˠ] becoming [dg] or [dɣ]). Along the same reasoning, they may have preserved a timing closer to that of a single segment. Such stability of timing patterns across time can be taken as further evidence for the phonological status of timing information.

While the current findings, together with previous studies on Georgian clusters, provide strong evidence for the perceptual role of timing lag in stop-stop sequences, further work is needed to determine whether similar mechanisms underlie the timing of other cluster types or hold cross-linguistically. Regardless of their perceptual motivation, the accumulating evidence in the literature indicates that timing differences constitute part of language-specific phonological knowledge. The role of timing in phonological grammar has indeed become increasingly clear across languages and different types of phonological contrasts. Gafos (2002) demonstrated the role of temporal coordination in Moroccan Arabic templatic word formation, becoming the first study to develop a full-fledged formal analysis incorporating gestural timing. Since then, the phonological role of timing (whether gestural or not) has found additional support, in particular in the instantiation of phonological contrasts. Tone contrasts in several languages, for example, have been shown to consist of tonal units that differ exclusively in their timing (e.g., Remijsen & Ayoker, 2014, for Shilluk; Svensson Lundmark et al., 2021, for Swedish; Karlin, 2022, for Serbian). Segmental contrast between complex segments and sequences of segments has been shown to rely on the different timing of the same component articulatory gestures (Shaw et al., 2021).

Our view of the role of timing in phonology, as supported by the Georgian data examined here, most closely resembles the one presented by Gafos et al. (2020), in three aspects:

The results of our own study provide perceptually motivated explanations for the wide variability of overlap patterns in Georgian in Pouplier et al. (2022). The consonant sequences compared in that study were exclusively those that occurred in all of the seven languages under investigation (obstruent-liquid, sibilant-obstruent, and /kn/, /gn/, for a subset of the languages). A plausible interpretation for the reduced overlap measures found in some of the Georgian data is that they are motivated by the presence of stop-stop sequences in the language, the sequences that require close control of timing for recoverability reasons. The variable timing lag in Georgian, expanding further into the reduced overlap range than the other languages, can be seen as an optimization solution for the temporal unfolding of the component gestures. Importantly, reduced overlap (long lag) is not simply generalized across all CC sequences in Georgian. If it were, then presumably a sequence like /sp/ would no longer be perceived as two adjacent consonants. Instead, a broader overlap range is the preferable compromise such that, depending on their gestural composition, sequences spread out between the increased overlap (shorter lag) range, and reduced overlap (longer lag).

To sum up, the current study provides perceptual evidence for incorporating timing lag into phonological representations. The perceptual evidence has explanatory value, as it allows us to probe the relationship between vocal tract organization and linguistic knowledge. Taken together with the articulatory data (Pouplier et al., 2022), the current findings offer us a glimpse of how speakers manipulate vocal tract organization reflecting linguistic structure, and how listeners, in turn, use the temporal properties to recover the hierarchical, as well as the segmental, information.