Emotional Speech Recognition in Human-Computer Interaction Using Graph Neural Networks

A system that recognises emotion in speech for human-computer interaction using graph neural networks.

A system that recognises emotion in speech for human-computer interaction using graph neural networks. 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 to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses PyTorch Geometric, Speech Recognition API, Python, Hugging Face Transformers, and spaCy, with a clear project structure, setup instructions and sample data so that it can be run and extended.