# Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech Translation

arXiv: https://arxiv.org/abs/1904.11024 · HTML: https://mlsanity.com/paper/1904.11024/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Error-driven Fixed-Budget ASR Personalization for Accented Speakers](https://mlsanity.com/paper/2103.03142/) (2021)
- [A Text-To-Text Alignment Algorithm for Better Evaluation of Modern Speech Recognition Systems](https://mlsanity.com/paper/2509.24478/) (2025)
- [A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition](https://mlsanity.com/paper/2605.03671/) (2026)
- [Multi-task Pretraining for Enhancing Interpretable L2 Pronunciation Assessment](https://mlsanity.com/paper/2509.16876/) (2025)

## Abstract (arXiv)

We propose a variation to the commonly used Word Error Rate (WER) metric for speech recognition evaluation which incorporates the alignment of phonemes, in the absence of time boundary information. After computing the Levenshtein alignment on words in the reference and hypothesis transcripts, spans of adjacent errors are converted into phonemes with word and syllable boundaries and a phonetic Levenshtein alignment is performed. The aligned phonemes are recombined into aligned words that adjust the word alignment labels in each error region. We demonstrate that our Phonetically-Oriented Word Error Rate (POWER) yields similar scores to WER with the added advantages of better word alignments and the ability to capture one-to-many word alignments corresponding to homophonic errors in speech recognition hypotheses. These improved alignments allow us to better trace the impact of Levenshtein error types on downstream tasks such as speech translation.