Child Speech Data
Child-specific pronunciation,
fluency, hesitation, and repeated speech
Why Child Data Matters
The hardest challenge in AI for children
is understanding children.
Children's language is different from adults’ language.
Children pronounce differently, make different mistakes, hesitate differently, and respond differently.
In particular, English speech from non-native children
is difficult to understand accurately using general speech data or adult-centered language data alone.
Non-Native Children’s Language Data
Rare non-native
children's language
learning data
ELEA is built on data accumulated
in real educational settings from
non-native children ages 5–13,
including speech, writing, errors, and responses.
These are not merely voice recordings.
We accumulate language data
in real learning contexts,
connected to textbooks, levels,
learning objectives, and lesson stages.
Child Writing Data
Sentence construction, spelling and grammar,
and stage-specific writing errors
Error & Response Patterns
Incorrect answers, retries, self-correction,
and response flows by question type
Learning Context Data
Textbooks, levels, lesson stages,
question types, and learning objectives
Data to ELM
From child language data to ELM
We go beyond owning data and turn it into real learning experiences.
Accumulated child language data improves ELM and the assessment engine,
then returns to the product as more precise interaction and learning feedback.
Speech, writing, errors, and responses
Analysis of patterns in children's language, errors, and responses
Improvement of the child-specific language model and assessment engine
More precise interactions and learning feedback
CHILD-SPECIFICLEARNING
LOOP
LOOP
Data Improvement Loop
A data flywheel that gets smarter
with every learning interaction
Data accumulated in real educational settings
drives improvements to ELM and the assessment engine,
then moves through validation and product deployment
to create better learning experiences and generate new data.