The estimated overall WER e-WER was 25.3% for the three hours test set, while the actual WER was 28.5%.This is not a bill. Our system achieves 16.9% WER root mean squared error (RMSE) across 1,400 sentences. We report results for the two features black-box and glass-box using unseen 24 Arabic broadcast programs. Our e-WER framework uses a comprehensive set of features: ASR recognised text, character recognition results to complement recognition output, and internal decoder features. 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. %X 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. %I Association for Computational Linguistics %S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) %T Word Error Rate Estimation for Speech Recognition: e-WER The estimated overall WER e-WER was 25.3% for the three hours test set, while the actual WER was 28.5%. 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. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)Īssociation for Computational Linguistics Word Error Rate Estimation for Speech Recognition: e-WER
#SPEECH TIMER ESTIMATOR MODS#
Cite (Informal): Word Error Rate Estimation for Speech Recognition: e-WER (Ali & Renals, ACL 2018) Copy Citation: BibTeX Markdown MODS XML Endnote More options… PDF: Code = "Word Error Rate Estimation for Speech Recognition: e-.", Association for Computational Linguistics.
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In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 20–24, Melbourne, Australia. Word Error Rate Estimation for Speech Recognition: e-WER. Anthology ID: P18-2004 Volume: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) Month: July Year: 2018 Address: Melbourne, Australia Venue: ACL SIG: Publisher: Association for Computational Linguistics Note: Pages: 20–24 Language: URL: DOI: 10.18653/v1/P18-2004 Bibkey: ali-renals-2018-word Cite (ACL): Ahmed Ali and Steve Renals.
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Abstract 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.