Spoken Language Assessment

Spoken language assessment (SLA) aims to automatically evaluate various aspects of speech, including pronunciation, fluency, grammar, and even cognitive function, moving beyond traditional manual or semi-automatic methods. Current research focuses on leveraging deep learning models, such as large language models (LLMs), transformer-based architectures, and self-supervised learning (SSL) approaches like Wav2Vec2, often incorporating graph-based modeling for improved coherence analysis. These advancements offer the potential for faster, more objective, and scalable assessment tools across diverse applications, from second-language proficiency testing to clinical diagnosis of speech disorders and cognitive decline.