arXiv:2606.01820v1 Announce Type: new Abstract: Fine-grained morphosyntactic error annotation is important in clinical and developmental language research, yet it is labour-intensive, expert-dependent, and difficult to scale. We present TalkTag, an LLM-based lightweight tool fine-tuned to automate CHAT-style error annotation in spoken-language transcripts. Developed under conditions of extreme data scarcity using children's narrative data, the system shows the feasibility of linguistic analysis in low-resource settings. Our evaluation demonstrates that TalkTag produces encouragingly precise an
The proliferation of advanced LLMs enables new applications for fine-grained linguistic analysis, particularly in challenging, low-resource settings, marking a technical maturation point for speech processing.
Automating morphosyntactic error annotation dramatically reduces labor and expert dependency in clinical and developmental language research, accelerating diagnostics and interventions.
The ability to accurately and scalably analyze spoken language errors without extensive human expertise lowers barriers to entry for research and clinical applications in linguistics.
- · Clinical linguists
- · Speech pathologists
- · AI-driven language diagnostic companies
- · Children with language development issues
Wider adoption of automated language assessment tools in healthcare and education.
Improved early detection and personalized intervention strategies for language disorders across diverse populations.
Enhanced understanding of language acquisition and pathologies, potentially informing more effective language teaching methods and therapeutic approaches globally.
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