arXiv:2604.24278v3 Announce Type: replace-cross Abstract: Automatic speech recognition systems often produce confident yet incorrect transcriptions under noisy or ambiguous conditions, which can be misleading for both users and downstream applications. Standard evaluation based on Word Error Rate focuses solely on accuracy and fails to capture transcription reliability. We introduce an abstention-aware transcription framework that enables ASR models to explicitly abstain from uncertain segments. To evaluate reliability under abstention, we propose RAS, a reliability-oriented metric that balanc
The proliferation of ASR in critical applications necessitates more nuanced evaluation metrics beyond simple accuracy, especially as models become more complex and deployed in ambiguous conditions.
Improving ASR reliability is crucial for building trust in AI systems and preventing misinterpretation in sensitive applications, impacting user experience and the efficacy of downstream AI applications.
The introduction of abstention-aware frameworks and reliability-oriented metrics like RAS shifts the focus from purely maximizing accuracy to optimizing for trustworthy and interpretable model outputs.
- · ASR developers
- · Developers of AI agents
- · Industries relying on ASR (e.g., call centers, healthcare)
- · Users of voice interfaces
- · ASR systems with high confidence but low reliability
- · Evaluation methods solely focused on Word Error Rate (WER)
ASR systems will be designed and evaluated with greater emphasis on knowing when they don't know, leading to more robust performance in real-world scenarios.
This improved reliability will accelerate the adoption of ASR and voice-controlled interfaces in high-stakes environments, potentially reducing human intervention in certain tasks.
The concept of 'reliability-oriented metrics' could extend beyond ASR to other AI domains where model confidence and interpretability are critical, influencing broader AI safety and trustworthiness paradigms.
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Read at arXiv cs.AI