01Live speaking practice
- Learner experience
- Speak, hear one clear cue, and retry while the attempt is still fresh.
- Product signal
- Streaming or recorded audio, then pronunciation, fluency and completeness fields for that turn.
Learning platforms
Give every learner useful speaking feedback, at any scale.
Add guided speaking practice, homework and online exams to language-learning products without putting a teacher behind every response.
Live or submitted audioIndividual and class reportsEnglish & Mandarin
Designed for
Language-learning apps, online schools and adaptive tutoring products
- 1:1
- feedback without a teacher behind every turn
- Live + async
- practice, homework and exam delivery
- EN + ZH
- two language-specific scoring engines
/three product moments
Practice, homework and adaptive follow-up on the same evidence.
The engine stays in the background. Your product decides whether this turn is a live drill, an assignment, or the next step in a tutoring path.
01
02Homework and online exams
- Learner experience
- Submit when ready. The result is waiting without a teacher scoring every clip.
- Product signal
- Async scoring, audio-quality checks and comparable results across assignments.
03Adaptive next step
- Learner experience
- The next prompt, model or explanation depends on this attempt—not a generic score.
- Product signal
- Word and phoneme evidence your product can map to a retry, a hint or a new item.
See the assessment engine, without the agent layer.
Choose the product SDK experience or the MCP agent walkthrough based on how you plan to integrate.
/built for learning products
One assessment layer across practice, homework and exams.
Online products need speaking feedback that can follow the learner through live drills, submitted homework and higher-stakes checks—without a teacher scoring every clip, and without reducing the course to a single lifetime score.
Built for these activities
- Live drills
- Homework recordings
- Online oral exams
- Conversation practice
- Pronunciation correction
- Adaptive tutoring
- 01
Feedback while the attempt is still fresh
Return a usable cue quickly enough for a retry, a model, or a next item—rather than a transcript the product has to interpret later.
- 02
The same evidence in live and async flows
Stream a response or accept a recording. Completeness and audio-quality checks happen before pronunciation coaching is shown.
- 03
Fields your product can act on
Overall, fluency, word and phoneme evidence can power hints, adaptive sequencing, teacher reports and learner models.
/the product decision
Decide the next move after every spoken turn.
Decide what the learner should do next after every spoken response—not merely whether the audio was transcribed.
The situation
Large learner volumes make individual speaking feedback slow and inconsistent.
What changes
Immediate scores for learners and structured progress evidence for teachers and product teams.
/who acts on the evidence
Learners need a cue. Teachers need a view. Products need stable fields.
01
Learner
Needs: A clear cue while the attempt is still fresh.
Immediate feedback and a focused retry instead of a generic score.
02
Teacher
Needs: Visibility across more learners than they can hear live.
Progress and error patterns that make follow-up more targeted.
03
Product team
Needs: One assessment layer across several learning activities.
Stable fields that can power practice, reports and adaptive content.
/voice to action
Capture, score, explain, then choose the retry.
Each step has an owner. The engine returns evidence; the product decides what the learner sees next.
- 01
Frame the task
Provide the language, task type, reference text or rubric, and the feedback depth the activity needs.
- 02
Capture the response
Accept live streaming or a submitted recording and reject incomplete or unusable audio before coaching.
- 03
Read the evidence
Use overall, fluency, word and phoneme evidence from the English or Mandarin engine.
- 04
Choose the next move
Show encouragement, explain one priority, assign a retry or update the learner model.
/ready for product design
Define the task, the feedback policy and the progress model.
The English or Mandarin engine is only one layer. The product still owns activity design and how results are used.
Decision 01
Activity design
Define what a useful attempt looks like for drills, reading, conversation or open-ended speaking.
Decision 02
Feedback policy
Map evidence to age, level and teaching language instead of exposing the raw payload.
Decision 03
Progress model
Store comparable signals across activities without reducing learning to one lifetime score.
Responsible boundary
Audio quality and task completeness should be checked before the product presents pronunciation feedback.
Signals worth tracking
- More completed speaking turns
- Faster useful retries
- Teacher time spent on high-value intervention
/related scenarios
Continue by customer workflow.
Child-tuned English assessment
Young learner products
Speech assessment for young voices.
Open scenario
Face-to-face classes
Classroom training
Hear the whole room, not only the learner who raises a hand.
Open scenario
Tests & benchmarking
School assessment
Make speaking assessment consistent, explainable and easier to operate.
Open scenario