Comparative Evaluation of Multi-Agent Frameworks for Automated Multimodal Soft-Skills and Speech Coaching

A comparative evaluation of multi-agent frameworks for automated multimodal soft-skills and speech coaching.

A comparative evaluation of multi-agent frameworks for automated multimodal soft-skills and speech coaching. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are cooperating agents with defined roles and tools, fusion of more than one type of input, data preparation and exploration, model training and comparison of algorithms, and evaluation with standard metrics. The reference build uses LLM APIs, Speech Recognition API, Python, scikit-learn, and Pandas, with a clear project structure, setup instructions and sample data so that it can be run and extended.