The answer, according to a team spanning South Africa, France and the United States, appears to be yes, at least in part. Kyle Seakgwa of the University of the Western Cape, Sharon Peperkamp and Emmanuel Chemla of the Laboratoire de Sciences Cognitives et Psycholinguistique in Paris, and Derrin Pinto and Donny Vigil of the University of St Thomas in Minnesota, found that people who grew up speaking languages with phonemic clicks, such as Zulu or Xhosa, are better than monolingual English speakers at spotting paralinguistic clicks buried in English speech. The advantage is modest, and it emerges only under specific conditions, but its very existence offers a rare window into how linguistic experience shapes auditory perception in ways that go far beyond vocabulary and grammar.

To understand why this matters, it helps to distinguish the two kinds of clicks that linguists study. Phonemic clicks are full-fledged consonants in a small number of languages, estimated at roughly 0.5 percent of the world’s languages, nearly all of them in Africa. These sounds are acoustically and articulatorily complex: they involve a double place of articulation, with constrictions at both the front and back of the mouth held simultaneously, and they rely on a non-pulmonic airstream mechanism in which the tongue creates a vacuum and the release of the front constriction produces a characteristic popping or suction noise. Anyone who has listened to Xhosa or Zulu knows how salient these consonants are; they leap out of the speech stream even to untrained foreign ears.

Paralinguistic clicks are a different beast. They occur in many, perhaps all, languages, but they are not part of any phonemic inventory. Phonetically, they lie outside ordinary consonant systems; grammatically, they are not integrated into morphology or syntax; and semantically, they carry a narrow range of meanings, often tied to emotion or discourse management. A dental click in English can signal disapproval, while a click at the start of an utterance can mark that the speaker is searching for a word or launching a new sequence of talk. Researchers have documented such clicks in English, Spanish, French, German, Hebrew, Russian, Swedish and Mandarin Chinese, typically articulated with the tongue tip against the alveolar ridge or teeth, with a simultaneous velar closure. Less common variants include bilabial clicks, similar to blowing a kiss, and lateral clicks resembling the sound some cultures use to urge on a horse.

Despite their ubiquity, paralinguistic clicks are astonishingly hard to detect consciously. In a 2020 study, Vigil and Pinto showed native English speakers pairs of short video clips of semi-spontaneous speech, one containing a search click and one in which the click had been digitally silenced. Participants were asked whether they noticed any difference in the speaker’s oral expression and, if so, to report the timestamp. They succeeded in fewer than 20 percent of cases, a figure the authors argued was below chance. The result was puzzling because paralinguistic clicks are acoustically prominent: they typically occur during pauses and stand out against low-salience fillers like “um.” If listeners cannot hear such a conspicuous sound, something interesting must be happening in the perceptual system.

The new study set out to test whether speakers of click-using languages, which the researchers abbreviate as CU languages, show heightened sensitivity to these sounds. The team administered the same click detection task online, using the identical stimuli and procedure as the original experiment. Eight video clips of nine to ten seconds each, drawn from a scientific presentation by a native American English speaker, were paired so that one clip contained a paralinguistic search click and the other had the click silenced. Four additional distractor pairs contained no clicks at all. Participants watched each pair, separated by a five-second interval, indicated whether they perceived a difference, and if so reported the timestamp of that difference. Crucially, the click removal happened only in the audio track, a detail participants were deliberately not told.

The first experiment recruited 118 monolingual English speakers from the United States through the online platform Prolific, with the design and analyses pre-registered in advance. The researchers refined the original study’s statistical approach in two ways. First, they adopted a more stringent chance level of about 17 percent, reasoning that a participant has roughly a one-in-two chance of claiming any difference exists and approximately a one-in-three chance of landing the timestamp within one second of the click in a nine-to-ten-second clip. Second, they used bootstrapping with 1,000 resamples to estimate 95 percent confidence intervals, allowing a principled assessment of how plausible the null hypothesis really is. A detection was counted as timely only if the participant both correctly reported a difference and placed it within one second of the click.

The English speakers’ performance was strikingly poor. Mean timely detection on target items was just 11 percent, even lower than the 17.4 percent reported in the original classroom-based study, and statistically indistinguishable from the stringent chance level in both the opening condition, where clicks began a sentence, and the mid condition, where they appeared mid-sentence. Accuracy was also lower than in the original study for sentence-initial clicks, which the authors suggest may reflect the challenges of online testing compared with a controlled classroom setting. Yet the picture was not entirely bleak. The confidence interval for timely detections did not include zero, participants reported more differences in target pairs than in distractor pairs, and they described hearing the presence or absence of a click-like sound only in response to target pairs. The researchers conclude that English speakers’ capacity to detect paralinguistic clicks is weak, but not null.

The second experiment turned to South Africa, where Prolific offers no direct filtering by click language, so participants were initially selected as South-born and resident English-speaking multilinguals. Of 124 people tested, 54 reported speaking at least one CU language and were included in the analysis: 46 spoke Zulu, 16 Xhosa, 14 Sotho, 9 Swati and 3 Ndebele, with 27 speaking two or more of these languages. Using the same stimuli and procedure, these participants again showed overall low detection rates, performing no better than chance on sentence-medial clicks. But on sentence-initial clicks, their performance was significantly better than that of the monolingual English speakers, and their responses showed a clearer separation between target and distractor pairs, including click mentions that were entirely absent from distractor trials.

Why would the advantage be confined to clicks at the beginning of sentences? The researchers offer a tantalizing hypothesis rooted in phonology: click consonants in CU languages tend to appear at the beginning of words, so speakers of these languages may have tuned their attention to the positions where clicks are most likely to occur. The broader implication is that the presence of phonemic clicks in a person’s native language yields a perceptual advantage for non-linguistic clicks, at least when listening to a language the person speaks as a second language. The effect is small, and it survived despite an adversarial online setting and short stimuli, which makes its detection all the more notable.

Many questions remain. The team used only search clicks embedded in emotionally neutral speech, leaving open whether stance clicks in emotive-laden speech would be easier to catch. Audiovisual stimuli were chosen to prevent confusion between clicks and mouse clicks, yet audio-only presentation might make detection easier, as one study of the filler “um” suggests. And because all participants listened to English, a language they all knew, the researchers cannot rule out that detection would improve in an unknown language, where clicks are known to be highly salient even to listeners who speak nothing like them. What the study establishes is that the sounds we learn to treat as meaningful consonants reshape how we parse the incidental noises of conversation. In roughly half a percent of the world’s languages, clicks do grammatical work; everywhere else, they whisper at the edges of meaning. This research shows that where clicks live in a language determines, quite literally, what the ear learns to hear.

Subject of Research: Cross-linguistic differences in the auditory detection of paralinguistic click sounds in conversation

Article Title: Cross-linguistic variation in the detection of conversational click sounds

Article References: Seakgwa, K., Peperkamp, S., Pinto, D., Vigil, D., & Chemla, E. (2026). Cross-linguistic variation in the detection of conversational click sounds. Frontiers in Language Sciences, 5, Article 1932401. https://doi.org/10.3389/flang.2026.1932401

Image Credits: AI Generated

DOI: 10.3389/flang.2026.1932401

Keywords: click languages, paralinguistic clicks, phonemic clicks, cross-linguistic perception, Zulu, Xhosa, speech perception, bilingualism, psycholinguistics, phonetics, discourse markers, auditory detection

The study, published in Discover Education, was led by Gulumser Efeoglu and Büşra Yurdagül, who set out to answer a question that has grown urgent since online language classes became a permanent fixture of education. Previous research on anxiety in virtual settings has been strikingly divided. Some studies found that learners felt less isolated, less anxious, and more connected to teachers and peers when learning online, while others warned that technology itself, from mandatory cameras to unfamiliar platforms, could be anxiety-inducing. Rather than asking learners simply how nervous they felt, the Turkish team decided to look at what those learners actually said, treating the microscopic architecture of speech as a behavioral record of emotional state.

To do this, the researchers recruited 80 adult native Turkish speakers learning English, all of whom tested at the B1 intermediate level on the Oxford Placement Test, a step taken to ensure that differences in vocabulary knowledge would not contaminate the results. Half of the participants were studying in face-to-face settings, including a university School of Foreign Languages and a private language institute, while the other half attended online classes at the same private institute, which used platforms such as Zoom and Microsoft Teams. Participants ranged in age from 18 to 55, and each completed a battery of instruments: an adapted 18-item version of the Foreign Language Classroom Anxiety Scale, a standardized proficiency test, and a semi-structured interview conducted in Turkish to encourage candid self-expression.

The centerpiece of the methodology was a task drawn from the Language Situations method, designed to elicit naturalistic speech under controlled conditions. Participants watched a video depicting a fictional event, such as a traffic accident, and then narrated what they had seen as if reporting it to a police officer. This formal framing simulated an authentic communicative scenario while keeping the task identical across both groups. Every response was audio- or screen-recorded and transcribed verbatim, yielding a corpus of 6,790 seconds of speech, more than 113 minutes in total, and 7,029 fully uttered words. The face-to-face group contributed 3,101 seconds and 3,059 words, while the online group produced 3,689 seconds and 3,970 words, a difference that would later prove analytically important.

The researchers then turned to corpus analysis software called AntConc to hunt for discourse markers, the small words and phrases that speakers use to organize, connect, and cushion their utterances. Following a widely used four-part taxonomy, the team classified markers as structural, which organize discourse units, as in and, then, or first; referential, which link ideas together, as in because; cognitive, which signal mental processes and reformulations, as in well; and interpersonal, which convey viewpoint and shared knowledge, as in you know. Ambiguous words demanded careful adjudication. When a participant said it was just like so tense, the word so functioned as an ordinary intensifier, but in if the first car didn’t, like, stop, the word like operated as a genuine discourse marker signaling hesitation. In total, the analysis identified 660 discourse marker tokens across the two groups.

The first major finding was, in some ways, a non-finding. A Mann-Whitney U test comparing anxiety scores between the two groups revealed no statistically significant difference, with a negligible effect size. Learners in physical classrooms and learners behind screens reported roughly the same levels of speaking tension. This result runs counter to a body of earlier work suggesting that online environments inherently lower the affective barriers to speaking, and it complicates the popular assumption that virtual classrooms are a safe harbor for the socially anxious. Whatever protection the online medium may offer, it did not translate into measurably lower self-reported anxiety in this sample.

But the discourse marker data told a far more nuanced story. In the face-to-face group, two categories of markers showed a moderate negative correlation with anxiety: referential markers such as because, with a correlation coefficient of -0.343, and cognitive markers such as well, with a coefficient of -0.454. In plain terms, the more anxious a learner was in a physical classroom, the fewer of these idea-linking and self-monitoring words they produced. Interpersonal and structural markers, by contrast, were unaffected by anxiety levels in that setting. Strikingly, none of the four marker categories showed any significant correlation with anxiety in the online group, suggesting that the emotional signature of speech differs fundamentally depending on where the speaking takes place. Meanwhile, the online group used more markers overall, 384 tokens versus 276, though from a typologically narrower set, with and, then, I think, but, and okay topping the frequency list.

Fluency measures added another layer of contrast. When fluency was operationally defined as the relationship between total speaking time and total word count, the face-to-face group showed a stronger positive correlation, r = 0.819, than the online group, r = 0.584. This pattern indicates that learners in physical classrooms produced more continuous and verbose speech, sustaining their output over time more consistently. The researchers speculate that technical frictions inherent to online communication, such as connection problems and the absence of rich verbal and nonverbal cues, may slow the pace of virtual speech, even when anxiety itself is no higher. The interviews reinforced the emotional dimension of these patterns: across both groups, learners attributed their anxiety to personal traits such as limited vocabulary and low self-confidence, to social pressures including fear of being laughed at for mispronunciation and judgmental teacher feedback, and to cultural uncertainties about how their words would be perceived.

The practical implications are considerable. Because discourse marker use appears to track anxiety in face-to-face settings but not online, teachers could potentially use these small words as diagnostic tools, listening for a thinning of referential and cognitive markers as an early warning sign that a student is struggling emotionally. Conversely, explicit instruction in discourse markers might itself serve as a form of anxiety management, giving learners concrete linguistic resources for holding the floor and bridging hesitations. The authors suggest tailoring tasks to each context: using face-to-face classes to stimulate fluency, and online classes for explicit discourse marker teaching. They also recommend affective strategies such as mindfulness, which other recent work has linked to reduced speaking anxiety, alongside classroom cultures that frame pronunciation errors as a normal part of linguistic development rather than a source of shame.

The study’s authors are careful to note its limitations. The sample of 80 participants, while adequate for the statistical tests employed, was recruited through convenience sampling, and the formal police-report task may have constrained spontaneous marker use differently across the two settings. Future research, they suggest, should examine synchronous and asynchronous online formats, varied proficiency levels, and wider age ranges to test whether these patterns hold. Even so, the core insight stands as a compelling piece of the growing science of language and emotion: anxiety does not simply silence learners, it reshapes the fine-grained texture of what they say. The words we dismiss as verbal filler may, in fact, be among the most honest signals of how a speaker feels, and learning environments that understand this could help anxious speakers find not just their voice, but the connective tissue that keeps it flowing.

Subject of Research: Foreign language speaking anxiety and discourse marker use in online versus face-to-face English classes

Article Title: Foreign language speaking anxiety and discourse markers in face-to-face and online classes

Article References: Efeoglu, G., & Yurdagül, B. (2026). Foreign language speaking anxiety and discourse markers in face-to-face and online classes. Discover Education, 5(1), Article 1066. https://doi.org/10.1007/s44217-026-02220-3

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02220-3

Keywords: foreign language speaking anxiety, discourse markers, online learning, face-to-face classes, EFL, fluency, second language acquisition, language education, psycholinguistics, FLCAS, corpus analysis, Turkish learners