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New LLM tools automate speech and transcript error annotation

By PulseAugur Editorial · [5 sources] ·

Researchers have developed two new methods for automatically annotating errors in transcribed speech. One approach, Speech Translation Error Labelling (STEL), uses existing text-only and multimodal LLMs to identify errors in speech translations, though current systems achieve about half the precision of humans. The other method, TalkTag, employs a fine-tuned LLM to automate fine-grained morphosyntactic error annotation in spoken-language transcripts, proving effective even with limited data. AI

IMPACT Automating error annotation in speech and transcripts could accelerate research and development in natural language processing and clinical linguistics.

RANK_REASON The cluster contains two academic papers describing new methods for error annotation in speech and transcripts.

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AI-generated summary · Google Gemini · from 5 sources. How we write summaries →

COVERAGE [5]

  1. arXiv cs.CL TIER_1 English(EN) · Dominik Mach\'a\v{c}ek, Maike Z\"ufle, Ondrej Klejch ·

    Automatic Labelling of Speech Translation Errors

    arXiv:2606.06047v1 Announce Type: new Abstract: Errors in speech translations reduce trustworthiness of Speech Translation (ST) systems and can have serious consequences. Yet currently there is no established methodology for evaluating confidence and quality estimation of speech …

  2. arXiv cs.CL TIER_1 English(EN) · Ondrej Klejch ·

    Automatic Labelling of Speech Translation Errors

    Errors in speech translations reduce trustworthiness of Speech Translation (ST) systems and can have serious consequences. Yet currently there is no established methodology for evaluating confidence and quality estimation of speech translations. To initiate progress in this direc…

  3. arXiv cs.CL TIER_1 English(EN) · Shamira Venturini (Karlsruhe Institute of Technology, Karlsruhe University of Applied Sciences), Oliver Hennh\"ofer (Karlsruhe University of Applied Sciences), Steffen Kinkel (Karlsruhe University of Applied Sciences), Jannik Str\"otgen (Karlsruhe Univer… ·

    TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech

    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…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech

    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 i…

  5. arXiv cs.CL TIER_1 English(EN) · Jannik Strötgen ·

    TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech

    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 i…

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