Beyond Words: Building an AI Mental Health Monitor with HuBERT and Psycho-Acoustics
We often focus on what someone says, but in the realm of clinical psychology, how they say it is often more revealing. Subtle changes in speech—a slight tremor (jitter), a slowing tempo, or a flattened pitch—can be early indicators of depression or anxiety long before a user explicitly voices their distress. In this tutorial, we are building Psycho-Acoustic , a high-performance monitoring tool…
In the field of clinical psychology, non-verbal cues such as speech inflections can often provide early indications of mental health issues like depression or anxiety. This tutorial aims to build a Mental Health Monitor that uses the HuBERT model, HuggingFace Transformers, and Librosa to analyze non-verbal acoustic features from speech recordings.
The tutorial covers the entire process from extracting acoustic features to deploying the model for real-time monitoring via ONNX Runtime. The architecture involves preprocessing raw audio input, extracting features like fundamental frequency (F0), speech rate, and jitter using Librosa, and then applying HuBERT embeddings for more advanced deep learning analysis.
The final step involves running the model with ONNX Runtime for efficient inference during deployment.
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