TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech
For clinical and developmental language researchers, TalkTag offers a scalable tool to automate labor-intensive error annotation in low-resource settings.
TalkTag automates fine-grained morphosyntactic error annotation in transcribed speech, achieving precise annotation and identifying ambiguous cases, providing a scalable alternative to manual annotation.
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 annotation while effectively identifying instances where linguistic ambiguity makes automated tagging genuinely complex. In summary, with TalkTag, we provide a scalable alternative to manual error annotation and practically viable support for morphosyntactic error annotation.