Measure the signal
Short natural-speech tasks separate reductions, word boundaries, meaning contrasts, gist and detail.
For adults who know English—but lose it when people speak
English you know can still disappear in a movie.
EarShift maps the moment natural speech turns into blur: reductions, missing word boundaries, meaning-changing sounds, or details that vanish while a conversation keeps moving.
Tap an ear. Each voice hides a different listening trap.
8early research participants
3/3completed the latest instrumented pilot
4.7/5latest-pilot engagement
118complete trial records in the latest pilot
The research idea
A short test should predict the next miss—not just grade the last clip.
The current prototype uses listening responses to select targeted practice and checks performance on reserved recordings. The research goal is to predict future listening errors, distinguish vocabulary gaps from perception difficulties, and validate transfer across unfamiliar speakers.
Investigate the miss
Learners currently report what made a clip hard. Independent paired vocabulary checks are planned for the next research stage.
Test transfer
Training uses new examples. The final check uses reserved recordings and contexts; systematic new-speaker validation comes next.
The subtitle problem
Vocabulary is not always the bottleneck.
A learner may know every word on a subtitle and still fail to segment or recognize the same words at conversational speed. EarShift targets that gap between written knowledge and real-time perception. The current demo tests the measurement loop; it does not yet claim to make people stop using subtitles.
The words are known; their spoken shape changes.
Continuous speech hides where one word ends.
One missed sound can change a decision.
The conversation continues before the listener can recover.
A different job
Pronunciation apps train your mouth. EarShift studies what your ears predict.
Pronunciation appsSpeaking accuracy
Record, repeat, receive pronunciation feedback
Video-learning appsSupported comprehension
Watch authentic video with captions, vocabulary and replay tools
EarShiftResearch goal: unseen listening transfer
Develop and validate prediction, targeted practice and tests on new speech
Early evidence, honestly stated
The research flow runs. Learning impact is still unproven.
Eight compensated participants have completed two formative rounds. In the latest three-person pilot, all participants finished, event capture was complete and engagement averaged 4.7/5. Diagnostic accuracy was near ceiling, so this version is useful evidence about feasibility and test design—not proof of learning impact.
What we learned
The flow can capture item-, replay-, timing-, speaker- and transfer-level data without missing trials.
What must improve
Harder items, better-matched parallel forms and a broader learner range are required to measure real differences.
What comes next
A preregistered active-control study with unseen immediate and 24–72-hour delayed transfer.
From learner tool to research technology
Prove prediction first. Scale training second.
- NowEnglish learners
Validate whether a short diagnostic predicts errors in new movie-like speech.
- Phase IAdaptive learning
Compare uncertainty-aware personalized training with duration- and content-matched ordinary practice.
- Next marketWorkforce communication
Help schools and employers target comprehension risks in fast, speaker-variable English.
EarShift listening lab
Find where real English disappears for you.
Try the full research demo. Your responses help improve the instrument; the result is a listening profile, not a hearing or medical assessment.
Enter the listening lab