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HFEPX · Eval paper review

Ask, Don't Guess: Targeted Clarification for Speech Recognition Errors

Sankalp Parashar, Preethi Jyothi, Sunita Sarawagi

Published

Oct 17, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

20% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Oct 17, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Speech recognition in real time in far-field conversational settings still incurs significant errors. Identifying potential error spans during streaming ASR permits a recourse mechanism where the speaker can be asked to repeat the affected content under cleaner acoustic conditions. We propose \sysname, a new framework for online error localization, that identifies spans likely to benefit from this intervention. Our supervision uses clean audio to estimate how the recognizer's output might change under improved acoustic conditions. We probabilistically align noisy and clean decodes using a pair-HMM to obtain soft supervision. These soft targets train a counterfactual gain scorer by combining a suite of acoustic and language features to produce token-level scores, that are further grouped into spans for targeted recourse. An LLM uses the ASR transcript of the repetition to revise only the selected span. At a precision of $70\%$, \sysname achieves the highest recall by identifying up to 52\% more erroneous tokens compared to the strongest baseline. Targeted repetition yields relative WER reductions of up to 24\% under a budget that accounts for both interruption frequency and the lengths of re-uttered spans.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Speech recognition in real time in far-field conversational settings still incurs significant errors."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Speech recognition in real time in far-field conversational settings still incurs significant errors."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Speech recognition in real time in far-field conversational settings still incurs significant errors."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Speech recognition in real time in far-field conversational settings still incurs significant errors."

Reported Metrics

partial

Precision, Recall, Wer, Jailbreak success rate

Useful for evaluation criteria comparison.

"At a precision of $70\%$, \sysname achieves the highest recall by identifying up to 52\% more erroneous tokens compared to the strongest baseline."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

precisionrecallwerjailbreak success rate
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Speech recognition in real time in far-field conversational settings still incurs significant errors.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Speech recognition in real time in far-field conversational settings still incurs significant errors.
  • Identifying potential error spans during streaming ASR permits a recourse mechanism where the speaker can be asked to repeat the affected content under cleaner acoustic conditions.
  • We propose \sysname, a new framework for online error localization, that identifies spans likely to benefit from this intervention.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • We propose \sysname, a new framework for online error localization, that identifies spans likely to benefit from this intervention.

Why it matters for eval

  • Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: precision, recall, wer, jailbreak success rate