SURE-Tagger makes speech conditions easier to compare
SURE-Tagger: Structured Speech Representations for Compositional Evaluation and Targeted Data Selection · arXiv:2610.32198
In plain words
Speech recordings contain many details, such as who is speaking, whether voices overlap, and how noisy or echoing the room sounds. Captions can leave details out, so it is hard to tell whether a detail was absent or simply not mentioned.
SURE-Tagger is a framework that combines specialist model observations into records with named attributes and descriptions, marking uncertain details as unknown.
Its records scored higher than two comparison systems on a test of speech annotation, and one tested way of choosing training audio improved recognition for a single speech model.
The research
When a speech model makes more errors under some recording conditions than others, can researchers identify those conditions across datasets and choose training audio accordingly? Free-form captions make this difficult because missing details may be absent, overlooked, or uncertain.
SURE-Tagger turns specialist speech observations into explicit, queryable records, then tests their value for annotation and targeted speech-recognition training.
The authors compared SURE-Tagger with two audio-description systems on Tag-Bench, where systems record speech attributes and descriptions against verified references.
Across pooled existing-corpus recordings and controlled generated scenes, its score was higher than both comparisons, with all systems using the same language model to construct the final records.
They then used speech-condition groups to select about an hour of audio from a separate candidate pool. Qwen3-ASR-1.7B’s error fell relative to its unadapted result, supporting the use of these records for targeted data selection in this tested setting.
The adaptation result is specific to Qwen3-ASR-1.7B, the tested candidate pool, and a one-hour audio budget. The authors identify broader testing across models, languages, and recording conditions as future work.
What the evidence shows
- Make speech conditions explicit
SURE-Tagger is a speech annotation framework that creates records with explicit values for predefined speech attributes, supplementary open-ended descriptions, and an unknown state when evidence is insufficient. The paper addresses a practical measurement problem: free-form captions do not consistently mention the same attributes, so an omission cannot reliably mean that a condition is absent. Explicit values make conditions easier to compare and query across recordings.
- Compare annotation against references
The authors evaluated annotation quality with Tag-Bench, a test of how well systems record speech attributes and descriptions against verified references. It combines existing-corpus recordings with controlled generated scenes.
Across the two subsets pooled together, SURE-Tagger scored 77.28% on 29 equally weighted axes, compared with 60.93% for Qwen3-Omni-Flash and 58.20% for Qwen3-Omni-Captioner. All three used the same language model to construct final records from their different audio observations; the overall score also gives partial credit for explicit unknowns.
- Group recordings by conditions
The records can also be used to form cross-dataset groups that meet several speech conditions at once. A recording enters a group only when every requested attribute is confirmed; unknown values do not count as confirmed absence.
The authors further checked whether attribute-based groups tracked recognition errors. For one tested speech recognition model, SURE-Tagger groups were more associated with error differences than caption-derived or random groups, a diagnostic association rather than proof of true scene categories.
- Test a downstream use
Finally, the authors used condition-level errors to select examples from a separate candidate pool for further training. With about one hour of selected audio, Qwen3-ASR-1.7B’s overall error fell from 5.50% unadapted to 4.71% on average across three sampling seeds.
Caption-derived selection and random selection both averaged 6.17% in this setting. This links the representations to a tested improvement in data selection, bounded to the model, pool, and audio budget evaluated.
Deep
- Original paper authors
- Jing Peng, Huifei Wang, Weihan Chen, Shuo Gao, Bicheng Zhang
- Submitted to arXiv
- License
- arXiv non-exclusive