# A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2605.03671 · HTML: https://mlsanity.com/paper/2605.03671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 5 May 2026.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding](https://mlsanity.com/paper/2104.02138/) (2021)
- [Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric](https://mlsanity.com/paper/2110.05376/) (2021)
- [Evaluating the Usability of Automatically Generated Captions for People who are Deaf or Hard of Hearing](https://mlsanity.com/paper/1712.02033/) (2017)
- [A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts](https://mlsanity.com/paper/1801.09746/) (2018)
- [Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech Translation](https://mlsanity.com/paper/1904.11024/) (2019)
- [HATS: An Open data set Integrating Human Perception Applied to the Evaluation of Automatic Speech Recognition Metrics](https://mlsanity.com/paper/2604.27542/) (2026)
- [Qualitative Evaluation of Language Model Rescoring in Automatic Speech Recognition](https://mlsanity.com/paper/2604.27533/) (2026)

## Built on by (most cited first, 1 of 1)

- [Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study](https://mlsanity.com/paper/2608.25574/) (2026)

## Abstract (arXiv)

The most commonly used metrics for evaluating automatic speech transcriptions, namely Word Error Rate (WER) and Character Error Rate (CER), have been heavily criticized for their poor correlation to human perception and their inability to take into account linguistic and semantic information. While metric-based embeddings, seeking to approximate human perception, have been proposed, their scores remain difficult to interpret, unlike WER and CER. In this article, we overcome this problem by proposing a paradigm that consists in incorporating a chosen metric into it in order to obtain an equivalent of the error rate: a Minimum Edit Distance (minED). This approach parallels transcription errors with their human perception, also allowing an original study of the severity of these errors from a human perspective.