A toolkit for EMA annotation & analysis

Take raw kinematic recordings from hardware like the NDI Wave or Carstens AG501 and align them with TextGrid annotations to produce clean, per-utterance files ready for annotation or analysis.

Detect articulator target achievements (tongue tip, tongue body, lips, jaw) using deterministic heuristics, and hand-correct the output through a browser-based UI designed for lab assistants. Annotations can be placed on position profiles (MVIEW style) or on speed profiles.

Analyze your datasets against experimental hypotheses using generalized additive mixed models (GAMMs) or linear regression, directly in R or Python.