How do apps like Awababy analyse infant vocalisations?

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Apps like Awababy analyse infant vocalisations by classifying cries into emotional categories and matching them to suggested soothing responses
Awababy analyses infant vocalisations by sorting them into 11 emotional categories, then suggesting possible responses based on those classifications. The app was trained using more than 160,000 recordings of baby cries, alongside data on soothing techniques.
Training data and what the app was built to learn from
Awababy’s training used over 160,000 recordings of baby cries, paired with information about soothing techniques. This training setup is what enables the app to connect different vocal patterns to categories of distress and to response options.
The classification step: 11 emotional categories
Once a parent records or inputs an infant vocalisation, Awababy categorises the sound across 11 emotional categories. The output is not just a label; it is used to drive the app’s suggested next steps for responding to the baby.
Turning categories into “possible responses”
After classifying the cry into one of the 11 emotional categories, Awababy suggests possible responses. This means the app’s guidance is tied to the soothing-technique data used during training, rather than being limited to a single fixed interpretation.
What is not publicly detailed about the underlying AI
The available description does not provide specific technical details such as the exact machine learning model type or the feature-extraction method used to analyse the audio. What is clearly stated is the training scale (over 160,000 cry recordings), the categorisation structure (11 emotional categories), and the presence of suggested responses grounded in soothing-technique data.

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How do apps like Awababy analyse infant vocalisations?

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