# Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement

arXiv: https://arxiv.org/abs/2605.12107 · HTML: https://mlsanity.com/paper/2605.12107/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2026.

## Built on by (most cited first, 2 of 2)

- [Transcript-Supervised Post-Training of Generative Speech Enhancement on Real Recordings via Reinforce Adjoint Matching](https://mlsanity.com/paper/2609.29405/) (2026)
- [Perceptual Quality Loss or Loss of Perceptual Quality?](https://mlsanity.com/paper/2609.35054/) (2026)

## Abstract (arXiv)

Speech enhancement (SE) systems are typically evaluated using a variety of instrumental metrics. The use of automatic speech recognition (ASR) systems to evaluate SE performance is common in literature, usually in terms of word error rate (WER). However, WER scores depend heavily on the choice of ASR system and text normalization pipeline. In this paper, we investigate how modern ASR models correlate with human recognition of enhanced speech. A listening experiment reveals that modern ASR models with large-scale noisy training and embedded language models correlate more with human WER than simpler ones, with a transducer model providing the most reliable transcriptions. Nevertheless, we also show that these models' robustness to noise and use of context can be uninformative to an acoustics-focused evaluation of enhancement performance.