Word Error Rate Estimation for Speech Recognition: e-WER
Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we propose a novel approach to estimate WER, or e-WER, which does not require a gold-standard transcription of the test set. Our e-WER framework uses a comprehensive set of features: ASR recognised text, character recognition results to complement recognition output, and internal decoder features. We report results for the two features; black-box and glass-box using unseen 24 Arabic broadcast programs. Our system achieves 16.9% WER root mean squared error (RMSE) across 1,400 sentences. The estimated overall WER eWER was 25.3% for the three hours test set, while the actual WER was 28.5%.
Other Information
Published in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.18653/v1/p18-2004
History
Language
- English
Publisher
Association for Computational LinguisticsPublication Year
- 2018
License statement
This Item is licensed under the Creative Commons Attribution 4.0 International License.Institution affiliated with
- Hamad Bin Khalifa University
- Qatar Computing Research Institute - HBKU