| Challenge: | Automatic Speech Recognition (ASR) transcription errors are often assessed using metrics that compare them with a reference transcription. |
| Approach: | They propose to categorize transcription errors into four levels of severity based on objective linguistic criteria, contextual patterns, and the use of content words as the unit of analysis. |
| Outcome: | The proposed evaluation categorizes errors into four levels of severity based on objective linguistic criteria, contextual patterns, and the use of content words as the unit of analysis. |
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| Challenge: | Word error rate (WER) has been used for automatic speech recognition (ASR) evaluations for English datasets for many years. |
| Approach: | They propose to use the character error rate as the primary metric in multilingual ASR evaluation to account for morphologically complex languages. |
| Outcome: | The character error rate (CER) is the primary evaluation metric in multilingual ASR evaluation. |
Word Error Rate Estimation for Speech Recognition: e-WER (P18-2)
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| Challenge: | Automatic speech recognition (ASR) systems require manual transcription of test data to compute the word error rate (WER). |
| Approach: | They propose an approach to estimate word error rate (e-WER) that does not require a gold-standard transcription of the test set. |
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WER-BERT: Automatic WER Estimation with BERT in a Balanced Ordinal Classification Paradigm (2021.eacl-main)
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| Challenge: | Automatic Speech Recognition (ASR) systems are evaluated using Word Error Rate (WER) a higher WER means a lower percentage of errors between the ground truth and the transcription of the system. |
| Approach: | They propose a new balanced paradigm for automatic Word Error Rate estimation using a Librispeech dataset and a Google Cloud's Speech-to-Text API. |
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Language-specific Effects on Automatic Speech Recognition Errors for World Englishes (2022.coling-1)
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| Challenge: | Existing systems are not able to meet the needs of speakers of different demographic groups. |
| Approach: | They propose to analyze the performance of Otter’s automatic captioning system on native and non-native English speakers of different language background through a linguistic analysis of segment-level errors. |
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CEASR: A Corpus for Evaluating Automatic Speech Recognition (2020.lrec-1)
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Malgorzata Anna Ulasik, Manuela Hürlimann, Fabian Germann, Esin Gedik, Fernando Benites, Mark Cieliebak
| Challenge: | Automatic Speech Recognition (ASR) systems are increasingly needed for research and practical applications. |
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Evaluating Open-Source ASR Systems: Performance Across Diverse Audio Conditions and Error Correction Methods (2025.coling-main)
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| Challenge: | Automated speech recognition (ASR) systems are able to transcribe spontaneous human conversations with high accuracy. |
| Approach: | They evaluate the accuracy of open source automatic speech recognition systems across conversational speech datasets and explore the potential of ASR ensembling and post-ASR correction methods to improve transcription accuracy. |
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WER we are and WER we think we are (2020.findings-emnlp)
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Piotr Szymański, Piotr Żelasko, Mikolaj Morzy, Adrian Szymczak, Marzena Żyła-Hoppe, Joanna Banaszczak, Lukasz Augustyniak, Jan Mizgajski, Yishay Carmiel
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That doesn’t sound right: Evaluating speech transcription quality in field linguistics corpora (2025.acl-short)
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| Challenge: | Automated speech recognition (ASR) is a popular tool for documenting languages, but field linguists do not have the data to train robust models. |
| Approach: | They propose to use fieldwork data to identify speech transcriptions that may be unsuitable for training ASR models. |
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WER We Stand: Benchmarking Urdu ASR Models (2025.coling-main)
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| Challenge: | This paper analyzes the performance of three ASR models for low-resource languages like Urdu . low-rural languages like urdu have significant gaps in accuracy and reliability . |
| Approach: | They evaluate the performance of three ASR models: Whisper, MMS, and Seamless-M4T . they present the first conversational speech dataset for benchmarking Urdu ASR systems . |
| Outcome: | The proposed model families outperform Whisper, MMS, and Seamless-M4T on two types of speech datasets. |
Automatic Speech Recognition System-Independent Word Error Rate Estimation (2024.lrec-main)
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| Challenge: | Word error rate (WER) is a metric used to evaluate the quality of transcriptions produced by Automatic Speech Recognition systems. |
| Approach: | They propose a hypothesis generation method for ASR system-dependent WER estimation . they use phonetically similar or linguistically more likely alternative words to generate hypotheses . |
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