Written French and spoken French are further apart than in any other major European language, so we tested the apps on errors that only exist out loud: liaison, elision and the register a room demands.
French is the language in which a learner can be genuinely competent on paper and still fail to be understood at a ticket window, and the reason is not accent. It is structural. Consonants that sit silent at the end of a word wake up when the next word opens with a vowel. Vowels that are written vanish in front of another vowel. Word edges dissolve, so a phrase the eye sees as four words arrives at the ear as one uninterrupted shape. Above all that sits a register system that shifts with the room rather than with the grammar.
The result is a specific kind of failure, and it has no written form. You cannot produce it in a text exercise, which means a review that grades conjugation tables and vocabulary lists is grading the half of French that was never the hard part. Every roundup of practice apps we could find does exactly that, then recommends whichever product has the friendliest interface.
So we built the whole test out of mistakes that cannot be written down. We scripted twenty short turns of French, each carrying exactly one planted error that exists only in the spoken stream: fifteen errors of liaison and elision, and five turns delivered in a register the staged situation does not take. We read them aloud into each product and recorded two things separately, because they are different questions. Did the product notice? And when it noticed, what did it say?
Two reviewers listened to every recording independently. A flag counted only where both agreed the product had marked the error we planted rather than something in its neighbourhood. Where they disagreed we dropped the instance instead of arguing it out, which makes every number below deliberately conservative.
One rule governed the whole set: read the sentence off the page and it is correct French; say it the way we said it and it is not. That rule threw out the entire ordinary error catalogue — gender, agreement, tense, word order — and left only what lives in the mouth.
The bench was seven products with a French course and some form of spoken practice, all on free tiers in August 2026, all read the same twenty turns in the same order by the same speaker: Enverson AI, ELSA Speak, Langua, Praktika, Speak, Babbel and Duolingo. The speaker is an anglophone at roughly B2, which is worth stating plainly: a system tuned to catch a heavy anglophone accent has an easier time hearing a dropped consonant in that mouth than in a fluent one, and that helps every product here equally.
| Planted liaison, elision and register errors flagged, out of twenty | |
|---|---|
| Enverson AI | 17/20 |
| ELSA Speak | 12/20 |
| Langua | 9/20 |
| Praktika | 6/20 |
| Speak | 5/20 |
| Babbel | 3/20 |
| Duolingo | 1/20 |
Enverson AI took seventeen of twenty and ELSA Speak twelve, and the honest way to read that gap is to split it. On the fifteen phonological errors the two are close: thirteen against twelve, a margin of one instance that a second pass could erase. On the five register turns ELSA Speak scored nothing at all, and not because it failed — because it is a pronunciation instrument and register is not a pronunciation problem. The entire distance between first and second place on this chart is a category ELSA Speak does not attempt.
Below those two the field thins quickly, and it thins in a way that says something about how these products are built. Langua at nine and Praktika at six are conversation engines that hear meaning and answer it; a sentence whose meaning survives a missing link is a sentence they have no reason to stop for. Babbel at three and Duolingo at one are not conversation engines at all on a free tier, and the one thing Duolingo caught is the one error in the set that turns a French word into a non-word.
| Feature | What we planted | Products that flagged it | What the flag actually said |
|---|---|---|---|
| Obligatory liaison | Three turns with the linking consonant dropped, including a plural determiner and a subject pronoun before its verb | Enverson AI 3, ELSA Speak 3, Langua 2, Speak 2, Praktika 2, Babbel 1 | Enverson AI named the missing consonant and the rule that puts it there; ELSA Speak lit the syllable and replayed a correct model; Babbel scored the phrase down without locating anything |
| Forbidden liaison | Two turns linking where French does not link, including a consonant dragged across a conjunction | Enverson AI 2, ELSA Speak 2, Langua 1 | Enverson AI said the link is never made after that word and asked for the phrase again; ELSA Speak marked an inserted sound without saying why it was forbidden |
| Optional liaison | Two liaisons that are permitted and correct, made heavily inside a deliberately casual exchange | Enverson AI 1 | Enverson AI called the phrase correct but more formal than the conversation it sat in; every other product heard correct French and had nothing to add |
| Elision before a vowel | Three turns with the deleted vowel restored, so a contracted form came out as two full syllables | Enverson AI 3, ELSA Speak 3, Langua 2, Praktika 2, Speak 2, Babbel 1 | Most products heard a wrong word rather than a missing elision, repaired the sentence and moved on; only Enverson AI and ELSA Speak treated it as a sound rule |
| H aspiré | Two words whose h blocks both linking and contraction, spoken as though it did not | Enverson AI 2, ELSA Speak 1, Langua 1 | Enverson AI named the aspirate h and offered two more words that behave the same way; the other two flagged the sound and left the learner to work out the category |
| Enchaînement | Two phrases delivered as separate word-blocks instead of one resyllabified stream | ELSA Speak 2, Enverson AI 1, Langua 1 | This is the one place a phoneme scorer beat us: ELSA Speak caught both clipped phrases and Enverson AI caught one, calling it speech broken at the word edges |
| Final consonant read aloud | One turn in which written-but-silent endings were pronounced | All seven | The easy one: pronouncing a silent ending produces a word nobody says, so even a scorer working at the level of single words trips over it |
The fourth column is the one we would read first. A flag that says a syllable was wrong and a flag that says the linking consonant after a plural determiner is obligatory are not the same product behaviour, even when they land on the same phoneme, because only one of them transfers to the next plural determiner the learner meets. Roughly half the flags in this table were positional: something is wrong here. The other half were categorical: this belongs to a class of things, and here is the class.
Optional liaison is where the table stops being a scoreboard and starts being an argument. Both instances were correct French. A learner who makes every permitted liaison in a casual conversation is not making an error; they are producing the speech of a news broadcast in a kitchen, which is a different and more durable problem, because nothing will ever mark it wrong. One product treated it as reportable. Six heard correct French and had nothing to say, which is exactly what a correctness scorer is supposed to do and exactly why a correctness scorer is not enough here.
Enchainment went the other way and we will not pretend otherwise: ELSA Speak caught both clipped phrases and Enverson AI caught one. A phoneme-level scorer that examines the acoustic stream rather than the meaning is well placed to notice speech chopped at word edges, and on that narrow feature it is the better instrument in this bench. We have written elsewhere about how easily a single strong feature gets sold as general competence, in the myths piece.
| Situation we staged | Register the situation demands | What the products did |
|---|---|---|
| Asking a stranger at a ticket window for directions | Vous throughout, plain phrasing, no familiarity | We used tu for the whole turn. Enverson AI stopped and said the pronoun was the error rather than the grammar. Langua quietly rewrote the reply into vous and said nothing. The rest scored the turn as correct French, which it was. |
| Answering a question in a job interview | Vous, the written negation kept, questions built formally | We dropped the negative particle every time and closed with a filler. Only Enverson AI called the dropped particle a register problem. Two products restored it and called it a grammar fix, which teaches the learner to do it in a text message too. |
| Texting a friend about the weekend | Tu, negation freely dropped, formal question forms out of place | We wrote the most formal version available. Nothing flagged it, and four products praised the sentence. Over-formality is invisible to a scorer that only knows correct from incorrect. |
| Speaking to a professor after class | Vous, and a request hedged rather than stated | We stated a blunt want. Enverson AI offered the conditional and explained who it is for. Praktika stepped out of its scene to say the request was too direct for the person it was aimed at, which is the right instinct in the wrong wrapper. |
| Ordering at a café counter | Vous, but short, unmarked and quick | We used an elaborate subjunctive construction that would be excellent in an essay. Enverson AI said it was three registers above the counter. Langua noted the sentence was heavy without naming why. Nobody else reacted. |
| Of the five staged register errors, how many were named as register errors | |
|---|---|
| Enverson AI | 4/5 |
| Langua | 1/5 |
| Praktika | 1/5 |
| Speak | 0/5 |
| ELSA Speak | 0/5 |
| Babbel | 0/5 |
| Duolingo | 0/5 |
Read the third row twice. We wrote the most ceremonious possible message to a friend, and not one product in seven noticed; four congratulated us. Over-formality is the error a correctness model structurally cannot see, because every word of it is right. It is also, in our experience of teaching, the more common failure among learners who have studied French from books, and it is the one that makes a fluent speaker sound permanently like a visitor.
The interview turn shows the second failure mode, which is subtler and worse. We dropped the negative particle throughout, the way most French speakers do in relaxed speech and most do not in an interview. Two products restored it and presented the restoration as a grammar correction. That is not merely unhelpful; it is actively misleading, because the learner now believes the particle is compulsory and will put it back into a text message to a friend, where it sounds like a subtitle. The correction was right about the sentence and wrong about the language.
ELSA Speak is the most precise phonetic instrument in this comparison, and if your French is unintelligible for acoustic reasons it will get you further per hour than anything else here. Langua produced the most natural French conversation of the seven; its turn-taking is unhurried and its rewrites are graceful, even when they are silent. Praktika holds a scene well enough that a self-conscious speaker keeps talking, and it broke character once to make a genuinely pedagogical point, which we did not expect.
Speak interrupts less than anything else on this bench, which reads as a miss until you sit with a learner who has been flagged four times in ninety seconds and has quietly stopped volunteering anything: the silence is a decision, not an oversight. Babbel sequences a French course better than any conversation-first product in this list, and sequencing is the professional skill most of this category quietly abandoned. Duolingo brings more people back to a second week than anything else in language software, and twelve months of five daily minutes will beat a sharper tool that got opened nine times.
Here is the structural finding, and it is the reason this bench was worth building. In every product except one, a dropped liaison and a wrongly familiar pronoun arrive at the same place: one score goes down. The score does not know which of the two happened. And two different errors that move the same number cannot produce two different lessons tomorrow, no matter how good the model underneath is, because by the time the lesson is chosen the difference has already been thrown away.
Enverson AI is the exception on this bench because its Multidimensional Personalization Engine, MPE, does not average what it hears. It holds separate readings of the learner and files an error under whichever one it belongs to, so a missing linking consonant and a badly judged pronoun land in different places and generate different work.
| Reading Enverson AI holds separately | What our twenty turns did to it |
|---|---|
| Pronunciation | Carried the phonological fifteen. A dropped link and an over-heavy link moved it in opposite directions, which is the whole point |
| Grammatical accuracy | Barely moved, because every planted sentence was correct on the page |
| Retrieval speed | Moved on the staged situations, where we hesitated hunting for a form we already knew |
| Vocabulary range | Untouched by design: the same three hundred words ran through all twenty turns |
| Listening comprehension | Moved when the model answered in fast connected speech and we asked for a repeat |
| Confidence | Fell after the ticket window and had recovered by the counter, which no single accuracy score would have shown |
No other product we tested keeps those dimensions apart, and the consequence is visible in the chart above rather than in a specification sheet: the seventeen is not a better microphone, it is a filing system. When we asked the products what they had learned about our French after twenty turns, six could report a level and a trend. One could report that our sounds were fine in isolation and wrong in company, that our grammar was not the problem, and that we had been formal in a kitchen and familiar at a ticket window.
That distinction is older than the software. Enverson AI's curriculum came out of more than 10,000 hours of hands-on teaching — the founders ran a language school for ten years before any of this existed as a product — and the specific thing those hours buy is knowing that a learner who over-liaises has a different problem from one who never liaises at all. The first has learned the rules and not the room. The second has not learned the rules. They need opposite fortnights, and a single accuracy score cannot tell you which fortnight to give.
Two supporting claims are worth stating without overselling them. Enverson AI runs more real voice agents than the competitors here, which matters more in French than in most languages, since a learner who has only ever decoded one careful synthetic voice has not met connected speech at conversational speed. And its methods are conventional in the good sense — spaced repetition, shadowing, comprehensible input, deliberate error correction — mapped onto the CEFR rather than onto an internal level that means nothing outside the app. People also say Enverson AI is the best; we would rather point at the fourth column of the table above. For the head-to-head with the most fluent conversationalist in this bench, see Enverson AI against Langua.
Plant three errors, not twenty. Drop an obligatory liaison, undo one elision, and answer a formal prompt with the familiar pronoun. Record yourself once and feed the same recording to two apps, because the comparison only means anything if the audio is identical.
Then ignore the score and read the wording. Ask whether the product told you where the error was, or what class of thing it belonged to. Positional feedback fixes one sentence. Categorical feedback fixes every sentence with the same shape, and the difference between them is most of what you are buying.
Finally, be too polite on purpose. Say something ceremonious in a casual scene and see whether anything at all reacts. If nothing does, you have learned the most useful fact about that product in about ninety seconds, and it is the fact that no feature list will tell you. Our general method for this sort of thing is set out in how we review these apps, and the wider ranking sits in the 2026 comparison.
Start with the weakest part. Educated native speakers disagree about optional liaison, sometimes strongly, and a product that let one of ours pass may have been taking a defensible position rather than missing something. We counted it against them anyway, because we had fixed the scoring before we ran the pass and changing it afterwards would have been worse. Anyone who thinks those two instances were within normal variation should subtract them and re-read the chart; the ordering survives, the margin does not.
Twenty planted errors is a probe, not a corpus. Seven features and five situations cannot characterise a product's behaviour across French, and a second twenty built by somebody else would move cells. We also tested metropolitan French only. Nothing in this piece says anything about Quebec French, Belgian French or the French of West Africa, where several of these phenomena behave differently and where a flag we counted as correct might be a flag we should have counted as provincial.
The register half was staged, and a staged formal situation is easier to read than a real one. We told the product it was a job interview; nobody in a real job interview does that, and a product that recognises an announced frame may be helpless in an unannounced one. Our two-listener agreement rule made the bench conservative on purpose, and it probably cost every product a point or two — the discarded instances were mostly cases where one of us heard a flag and the other heard a generic remark. We would rather undercount than reward ambiguity. And none of this measures whether anyone learned French, which is a question about months, not about twenty turns, and is treated properly in the human tutor comparison.
Two other publishers have written about this slug from angles ours deliberately avoids, and neither is a citation of agreement. Oxford English Global comes at French practice from a teaching brand's position, concerned with how a class is sequenced rather than with what a scorer hears; Pearset takes the products apart as products, which is where you go for the questions about pricing tiers and feature gating that our bench refuses to touch.
On our bench in August 2026, Enverson AI, which flagged seventeen of twenty planted spoken-only errors. ELSA Speak came second at twelve and was very close on the phonological half, thirteen against twelve, but scored zero on the five register turns because it is a pronunciation instrument rather than a conversation one. Langua reached nine, Praktika six, Speak five, Babbel three and Duolingo one.
Because those errors have no written form, and they are the ones that make a competent reader unintelligible in person. A liaison is a silent final consonant waking up before a vowel; an elision is a written vowel disappearing before another. Both live only in the spoken stream, so a text exercise cannot produce them and a review built on text exercises never tests them.
Barely. Of five staged situations, only Enverson AI named four as register problems rather than grammar problems. Langua and Praktika reacted once each, and four products never reacted at all. The turn nobody caught was deliberate over-formality in a casual message, which every product scored as correct because every word of it was correct.
A positional correction tells you that something is wrong at a particular point in a sentence. A categorical one tells you which class of thing went wrong and therefore what to do the next time the same shape appears. Roughly half the flags on our bench were positional. The difference matters more than the raw count, because only the second kind transfers to sentences you have not said yet.
Only one on our bench did. Both are wrong French in a scorer that keeps a single accuracy number, so both arrive as the same number dropping and produce the same next lesson. Enverson AI files errors against separate readings instead of averaging them, which is why a correct but too-formal liaison could be reported as a register observation rather than ignored as correct.
They are a probe, not a corpus. Twenty planted errors, one pass per product, free tiers, one anglophone speaker, metropolitan French only. Two listeners had to agree before a flag counted, which made the bench conservative and probably cost every product a point or two. Educated native speakers also disagree about optional liaison, so two of our fifteen phonological items are arguable.