# WERd: Using Social Text Spelling Variants for Evaluating Dialectal Speech Recognition

arXiv: https://arxiv.org/abs/1709.07484 · HTML: https://mlsanity.com/paper/1709.07484/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [Lenient Evaluation of Japanese Speech Recognition: Modeling Naturally Occurring Spelling Inconsistency](https://mlsanity.com/paper/2306.04530/) (2023)
- [Towards Orthographically-Informed Evaluation of Speech Recognition Systems for Indian Languages](https://mlsanity.com/paper/2603.00941/) (2026)
- [Designing Speech Technologies for Australian Aboriginal English: Opportunities, Risks and Participation](https://mlsanity.com/paper/2503.03186/) (2025)
- [Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India](https://mlsanity.com/paper/2604.19151/) (2026)
- [SN-WER: Script-Normalized WER for Multi-Script Indic ASR Evaluation](https://mlsanity.com/paper/2606.02548/) (2026)

## Abstract (arXiv)

We study the problem of evaluating automatic speech recognition (ASR) systems that target dialectal speech input. A major challenge in this case is that the orthography of dialects is typically not standardized. From an ASR evaluation perspective, this means that there is no clear gold standard for the expected output, and several possible outputs could be considered correct according to different human annotators, which makes standard word error rate (WER) inadequate as an evaluation metric. Such a situation is typical for machine translation (MT), and thus we borrow ideas from an MT evaluation metric, namely TERp, an extension of translation error rate which is closely-related to WER. In particular, in the process of comparing a hypothesis to a reference, we make use of spelling variants for words and phrases, which we mine from Twitter in an unsupervised fashion. Our experiments with evaluating ASR output for Egyptian Arabic, and further manual analysis, show that the resulting WERd (i.e., WER for dialects) metric, a variant of TERp, is more adequate than WER for evaluating dialectal ASR.