Developers can now turn their own voice into a health metric with a DIY Speech Emotion Recognition tool built on Svelte and Keras. By extracting Mel‑Frequency Cepstral Coefficients from live audio, the lightweight CNN model predicts stress, anxiety, or calmness in real time. The result is a dashboard that turns a frantic stand‑up into actionable data.

The pipeline starts at the microphone, feeds audio into a Svelte frontend that uses the Web Audio API to capture sound. On the server, Librosa turns the waveform into MFCCs, pads the sequence, and feeds it into a 1‑D CNN built with Keras. The model outputs a stress probability that the dashboard visualizes and can trigger a break reminder.

While the prototype runs locally, scaling to millions demands robust data pipelines and model serving. The author points readers to the WellAlly Tech Blog for enterprise‑grade patterns, and suggests training on the RAVDESS dataset for higher accuracy. Future work could hook the system into Zoom or Teams APIs to generate post‑meeting burnout reports.