KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing
Seonmin Koo, Chanjun Park, Jinsung Kim, Jaehyung Seo, Sugyeong Eo, Hyeonseok Moon, Heuiseok Lim · 2023
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).KE-BAP 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 textlevel 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.* Equally contributed, ‡ Corresponding author 1 Recognition accuracy is the measure of accurately perceiving phonemes as they are externally expressed, regardless of user input quality (Liao et al., 2022).Conventional (WER, CER) 0.45 KEBAP Error types Explainability Noise Type Description Washer/dryer machine Home appliances Vacuum cleaner Difficulty in recognition due to ambient electrical appliance noise.Motorcycle Siren Individual transportation Honk Difficulty in recognition due to surrounding individual transportation noise.Road side Street Crowd Difficulty in recognition due to the surrounding street noise.Conversation Cafe/restaurant Non-conversation Challenges in perception due to the noise in cafes/restaurants.Traditional market Market/shopping mall Shopping mall Difficulties in perception caused by the noise in markets/shopping malls.Subway platform Inside the subway Inside the train (STR/KTX) Public transportation Inside the bus Difficulty in recognition due to surrounding public transportation noise.Train terminal waiting room Terminal Bus terminal waiting room Challenges in perception due to the noise at terminals.Outdoor construction site Construction site Indoor construction site Difficulties in perception caused by the noise at construction sites.processing process Factory Assembly process Difficulties in perception caused by the noise in factories.Sound of rain Nature ambient Sound of the waves Challenges in perception due to natural ambient noise.Noisy environment Etc.Artificial mechanical sound In cases where external noise is present, although not falling into the aforementioned categories.