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    <abstract>Automatic Speech Recognition (ASR) systems are instrumental across various applications, with their performance being critically tied to user satisfaction. Conventional evaluation metrics for ASR systems produce a singular aggregate score, which is insufficient for understanding specific system vulnerabilities. Therefore, we aim to address the limitations of the previous ASR evaluation methods by introducing the Korean Error Explainable Benchmark Dataset for ASR and Post-processing (KEBAP). KEBAP enables comprehensive analysis of ASR systems at both speech- and text levels, thereby facilitating a more balanced assessment encompassing speech recognition accuracy and user readability. KEBAP provides 37 newly defined speech-level resources incorporating diverse noise environments and speaker characteristics categories, also presenting 13 distinct text-level error types. This paper demonstrates detailed statistical analyses of colloquial noise categories and textual error types. Furthermore, we conduct extensive validation and analysis on commercially deployed ASR systems, providing valuable insights into their performance. As a more fine-grained and real-world-centric evaluation method, KEBAP contributes to identifying and mitigating potential weaknesses in ASR systems.</abstract>
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%0 Conference Proceedings
%T KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing
%A Koo, Seonmin
%A Park, Chanjun
%A Kim, Jinsung
%A Seo, Jaehyung
%A Eo, Sugyeong
%A Moon, Hyeonseok
%A Lim, Heuiseok
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F koo-etal-2023-kebap
%X Automatic Speech Recognition (ASR) systems are instrumental across various applications, with their performance being critically tied to user satisfaction. Conventional evaluation metrics for ASR systems produce a singular aggregate score, which is insufficient for understanding specific system vulnerabilities. Therefore, we aim to address the limitations of the previous ASR evaluation methods by introducing the Korean Error Explainable Benchmark Dataset for ASR and Post-processing (KEBAP). KEBAP enables comprehensive analysis of ASR systems at both speech- and text levels, thereby facilitating a more balanced assessment encompassing speech recognition accuracy and user readability. KEBAP provides 37 newly defined speech-level resources incorporating diverse noise environments and speaker characteristics categories, also presenting 13 distinct text-level error types. This paper demonstrates detailed statistical analyses of colloquial noise categories and textual error types. Furthermore, we conduct extensive validation and analysis on commercially deployed ASR systems, providing valuable insights into their performance. As a more fine-grained and real-world-centric evaluation method, KEBAP contributes to identifying and mitigating potential weaknesses in ASR systems.
%R 10.18653/v1/2023.emnlp-main.292
%U https://aclanthology.org/2023.emnlp-main.292
%U https://doi.org/10.18653/v1/2023.emnlp-main.292
%P 4798-4815

Markdown (Informal)

[KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing](https://aclanthology.org/2023.emnlp-main.292) (Koo et al., EMNLP 2023)

• KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing (Koo et al., EMNLP 2023)

ACL

• Seonmin Koo, Chanjun Park, Jinsung Kim, Jaehyung Seo, Sugyeong Eo, Hyeonseok Moon, and Heuiseok Lim. 2023. KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4798–4815, Singapore. Association for Computational Linguistics.