| Challenge: | a transcription portal for audio files based on automatic speech recognition (ASR) is implemented in the CLARIN resources research network and intended for use by non-technical scholars. |
| Approach: | They propose a transcription portal for audio files based on automatic speech recognition in various languages. |
| Outcome: | The proposed transcription portal is implemented in the CLARIN resources research network and intended for use by non-technical scholars. |
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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. |
| Outcome: | The proposed measures can be used to identify transcriptions with characteristics common in field data but could be detrimental to ASR training. |
Improved Transcription and Indexing of Oral History Interviews for Digital Humanities Research (L18-1)
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| Challenge: | Existing methods to improve transcription and indexing quality of Oral History interviews are not available. |
| Approach: | They propose to use a German Oral History test-set to improve transcription and indexing quality . they propose to combine acoustic modeling techniques with sophisticated neural networks . |
| Outcome: | The proposed system reduces word error rate by 28.3% on German Oral History test-set compared to baseline system . the Fraunhofer IAIS Audio Mining system can process long audio-files to automatically create time-aligned transcriptions. |
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool (2022.acl-srw)
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| Challenge: | Automated speech recognition (ASR) models are based on a corpus of audio recordings, but are often small or nonexistent for less common languages and dialects. |
| Approach: | This research proposal will develop a semi-automatic acoustic features extraction system that integrates phonetic transcripts with pronunciation dictionaries. |
| Outcome: | The proposed system will be used to improve language recognition and model feedback in less common languages and dialects. |
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. |
| Outcome: | The proposed methods highlight the need for robust error correction techniques and address demographic biases to enhance ASR performance and inclusivity. |
Using Automatic Speech Recognition in Spoken Corpus Curation (2020.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) is a new way to make audio-visual data accessible. |
| Approach: | They propose to use automatic speech recognition (ASR) to make audio-visual data accessible by systematic queries. |
| Outcome: | The proposed system has higher recognition scores for the north of Germany vs. lower scores for south of the country. |
Towards Building an Automatic Transcription System for Language Documentation: Experiences from Muyu (2020.lrec-1)
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| Challenge: | Language documentation is a rapidly growing field due to its urgency. |
| Approach: | They propose to use phoneme recognition to automatically recognize spoken languages and translate them to global languages. |
| Outcome: | The proposed tool performs better than existing methods with American English, Austrian German and Slovenian as source and target languages. |
Enabling Interactive Transcription in an Indigenous Community (2020.coling-main)
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| Challenge: | Existing methods for manual transcription are often in isolation from the speech community, and so we miss out on the opportunity to take advantage of the interests and skills of local people. |
| Approach: | They propose a transcription workflow which combines spoken term detection and human-in-the-loop to support speech transcription in almost-zero resource settings. |
| Outcome: | The proposed workflow is based on two endangered languages with zero-resource datasets. |
Evaluating Workflows for Creating Orthographic Transcripts for Oral Corpora by Transcribing from Scratch or Correcting ASR-Output (2024.lrec-main)
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| Challenge: | Automated speech recognition systems can reduce transcription effort, but few studies have evaluated this potential. |
| Approach: | They compare efforts for manual transcription vs. automatic correction of ASR-output . they use audio recordings from varying settings to create orthographic transcripts . |
| Outcome: | The proposed methods reduce transcription time by 7 times on average for selected data and transcription conventions compared with corrected transcripts . the more complex the primary data, the more time has to be spent on corrections - the paper concludes a similar study could be conducted in 2022 . |
Automatic Transcription Challenges for Inuktitut, a Low-Resource Polysynthetic Language (2020.lrec-1)
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| Challenge: | Inuktitut is one of the 60 Indigenous languages currently spoken in Canada . polysynthetic languages are often termed agglutinative when their morphemes have clear boundaries and thus are easily segmentable. |
| Approach: | They propose to use a corpus of 23 hours of transcribed oral stories to train automatic speech recognition in Inuktitut. |
| Outcome: | The proposed model shows that Inuktitut displays a much higher degree of polysynthesis than other agglutinative languages like Finnish or Turkish. |
Towards Processing of the Oral History Interviews and Related Printed Documents (L18-1)
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Zbyněk Zajíc, Lucie Skorkovská, Petr Neduchal, Pavel Ircing, Josef V. Psutka, Marek Hrúz, Aleš Pražák, Daniel Soutner, Jan Švec, Lukáš Bureš, Luděk Müller
| Challenge: | a project aims to create an integrated archive of the recordings, scanned documents and photographs from totalitarian regimes in Czechoslovakia . the archive will be accessible online and provide multifaceted search capabilities . |
| Approach: | They propose to use automatic speech recognition and optical character recognition to build an archive of the interviews, scanned documents and photographs. |
| Outcome: | The proposed archive will be accessible online and provide multifaceted search capabilities. |