Year: 2025
Type: conference-paper
Abstract: Word error rate (WER) estimation aims to evaluate the quality of an automatic speech recognition (ASR) system’s output without requiring ground-truth labels. This task has gained increasing attention as advanced ASR systems are trained on large amounts of data. In this context, the computational efficiency of a WER estimator becomes essential in practice. However, previous works have not prioritised this aspect. In this paper, a Fast estimator for WER (Fe-WER) is introduced, utilizing average pooling over self-supervised learning representations for speech and text. Our results demonstrate that FeWER outperformed a baseline relatively by 14.10% in root mean square error and 1.22% in Pearson correlation coefficient on TedLium3. Moreover, a comparative analysis of the distributions of target WER and WER estimates was conducted, including an examination of the average values per speaker. Lastly, the inference speed was approximately 3.4 times faster in the real-time factor.
Institution: University of Sheffield
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408352164