BlaBla: Linguistic Feature Extraction for Clinical Analysis in Multiple Languages

TL;DR

BlaBla is an open-source Python library that extracts linguistically relevant features from speech in multiple languages, aiding clinical research on neurological and psychiatric disorders with validated tools and multi-language support.

Contribution

It introduces BlaBla, a unified, efficient framework for extracting clinically relevant linguistic features across many languages, validated on multiple diseases and demonstrated on real clinical data.

Findings

Validated features across 12 diseases

Supported multi-language analysis including three languages

Demonstrated clinical application with language disorder classification

Abstract

We introduce BlaBla, an open-source Python library for extracting linguistic features with proven clinical relevance to neurological and psychiatric diseases across many languages. BlaBla is a unifying framework for accelerating and simplifying clinical linguistic research. The library is built on state-of-the-art NLP frameworks and supports multithreaded/GPU-enabled feature extraction via both native Python calls and a command line interface. We describe BlaBla's architecture and clinical validation of its features across 12 diseases. We further demonstrate the application of BlaBla to a task visualizing and classifying language disorders in three languages on real clinical data from the AphasiaBank dataset. We make the codebase freely available to researchers with the hope of providing a consistent, well-validated foundation for the next generation of clinical linguistic research.