Abstract: The growing recognition of speech disorders’ detrimental effects on cognitive development and social adaptability has positioned intelligent assessment systems as a pivotal research priority in language rehabilitation.Conventional assessment approaches,reliant on manual observation and surface-level feature analysis,suffer from inherent limitations including subjectivity,inefficiency,and poor generalizability across diverse scenarios.In contrast,large language models(LLMs)-driven assessment technologies have significantly improved the objectivity and precision of pathological speech detection by integrating multimodal data with deep semantic modeling.This study comprehensively maps the technological evolution in speech disorder evaluation,tracing its progression from acoustic feature extraction to multimodal fusion architectures,with a focused analysis of Transformer-based multimodal integration methods and their groundbreaking applications in cross-linguistic adaptation and real-time intervention strategies.Comparative evaluations of mainstream datasets and metrics highlight LLMs’ superior performance in tasks like speech intelligibility quantification and semantic consistency verification.However,current methodologies encounter persistent challenges,such as inadequate dynamic adaptation of evaluation criteria and unaddressed biases in generative processes.Future research must prioritize the development of dynamically scalable assessment frameworks,leveraging ethical governance mechanisms and cross-modal contrastive learning to overcome high-dimensional semantic consistency modeling barriers.Simultaneously,enhancing clinical validation and privacy-preserving protocols will drive intelligent assessment technologies toward greater precision and fairness.Collectively,these advancements offer methodological blueprints for building adaptable,clinically viable systems,accelerating their scalable deployment in educational support and telemedicine ecosystems.

Key words: Large language models, Speech quality assessment, Voice disorder detection, Semantic consistency, Multi-modal fusion