How does SpeakShark work?
Four steps from sign-up to your first scored conversation
Pick a teacher, choose a topic, speak naturally, see your scores per phoneme. Thirty seconds to start your first session — no card, no setup, no install.
Choose your AI teacher
Pick from 4 AI teachers — each with a unique accent, personality, and teaching style. Whether you need a patient beginner coach or a challenging conversation partner, there's one for you.
- American, British, Australian, Canadian accents
- Beginner-friendly to advanced
- Switch anytime — no commitment
Ms. Sarah
American
Mr. James
British
Ms. Emily
Australian
Mr. Liam
Canadian
Pick a topic
Choose what you want to talk about. Daily conversations, travel scenarios, business meetings, job interviews — real situations you'll actually face.
- 6 topic categories
- New conversation every time
- Matched to your interests
Have a real conversation
Speak naturally into your mic. The AI responds to what you actually say — no scripts, no fill-in-the-blank. When something sounds off, you get an instant tip to fix it.
- Free-flowing, not scripted
- Real-time feedback on mistakes
- Think in English, not translate
Watch yourself improve
Track your speaking confidence over time. Weekly charts show exactly where you're getting better and where to focus next. Consistency beats intensity.
- Weekly progress charts
- Session-by-session tracking
- AI insights on your weak points
Methodology
How SpeakShark scoring actually works
Most apps tell you "AI scores your pronunciation" without saying what AI, what scoring, or how. Here is the honest stack behind every spoken response — written so a technical reader can verify the claims.
Step 1 — Speech-to-text
Production-grade speech recognition
Sub-second
transcription latency on short utterances
Every audio chunk is transcribed by an industry-leading multilingual automatic speech recognition (ASR) engine. The engine was trained on hundreds of thousands of hours of speech across many languages, accents, and noise conditions — which is why it handles non-native English well without needing a separate accent-specific model per learner.
Inference runs on a low-latency platform that delivers transcripts in well under a second on short utterances. That is what keeps the conversational loop feeling natural rather than transactional. Audio is streamed in roughly half-second intervals.
Step 2 — Conversational reply
Purpose-tuned conversational AI
4 personas
accent + CEFR + pedagogy per teacher
The transcript joins a rolling conversation context and is sent to a fast conversational language model. Each teacher has a system prompt that encodes their persona, target accent, CEFR difficulty band, and pedagogical strategy — gentle correction, scaffolded follow-ups, and vocabulary expansion relative to the learner's level.
We picked this model class because the conversational loop is cost-and-latency sensitive. A larger model would respond a few hundred milliseconds slower, which breaks the rhythm of real conversation.
Step 3 — 4-axis scoring
What we actually grade
Pronunciation
How closely each word's phonemes match the selected target accent. Errors are surfaced at the phoneme level — for example, the /θ/ in think coming out as /t/ — and a native audio sample is provided for comparison.
Grammar
Sentence-level grammaticality, focused on errors that block comprehension rather than minor stylistic variation. Native speakers make "errors" constantly without losing meaning; we grade against communication, not textbooks.
Fluency
Pace, hesitations, filler-word density, mid-sentence restarts. Distinct from accuracy — many learners are accurate but stilted, or fluent but inaccurate. Both axes matter for how natural you sound.
Vocabulary
Lexical range relative to your CEFR band, with suggestions for higher-register alternatives where appropriate. Encourages variety without pushing rare words that would sound forced.
Response feel
Why the loop feels conversational
~1-2 s
end-to-end response time
Real human conversation is sub-second turn-taking. If the loop takes four or five seconds, learners stop and the practice loses its rhythm. SpeakShark targets the under-two-second band — the threshold above which conversation stops feeling like conversation and starts feeling like a chatbot.
Across a typical home connection, the time from when you stop speaking to when the teacher starts replying is in the one-to-two second range. That budget is what made several engineering choices necessary — model selection, streaming protocols, and avatar lip-sync timing all serve that single number.
Numbers so far
What learners are actually doing
Approximate figures from SpeakShark's early cohort. Numbers refreshed manually each quarter — we publish round figures, not vanity precision.
+15 pts
avg. score gain
after 30 days of daily practice (10+ min/day cohort, internal data)
~12 min
avg. session
typical conversation length for engaged learners
4 accents
native targets
American, British, Australian, Canadian — one per AI teacher
320 topics
conversation prompts
across 10 categories from daily life to technology
Numbers above are rounded approximations from SpeakShark's internal analytics, intended as honest indicators rather than exact metrics. Individual results vary widely with practice consistency.
What the research says
Speaking-first is not a new idea
The case for speaking practice over grammar drilling is over a century old. Modern AI tools are new, but the pedagogy they implement is built on a long line of research and teaching practice. SpeakShark didn't invent this — we wrote software for what these people already proved.
“The first requisite is a sound knowledge of phonetics. Without it, the pupil's ear remains insensitive to differences in pronunciation, and his organs of speech are not trained to make them.
“The teaching of pronunciation must precede everything else, even the teaching of vocabulary, because nothing depresses a learner more than the sense that he cannot make himself understood.
“Anyone who is willing to take the trouble can learn to pronounce a foreign language reasonably well, provided he has access to a good model and a method of practising systematically.
“We acquire language in only one way: when we understand messages. We call this comprehensible input. The acquisition device fires automatically when the input is understood.
“The student should hear, speak, read, and write in the foreign language, in that order, just as a child learns its native tongue.
These are not slogans we invented. They are summaries of arguments these researchers made in print, citable in any university library. SpeakShark is the modern toolchain for ideas that were already correct a hundred years ago.