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| Autori principali: | , , |
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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2503.13031 |
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| _version_ | 1866912336729604096 |
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| author | Sponholz, Jakob Weilinghoff, Andreas Schopf, Juliane |
| author_facet | Sponholz, Jakob Weilinghoff, Andreas Schopf, Juliane |
| contents | In qualitative research, data transcription is often labor-intensive and time-consuming. To expedite this process, a workflow utilizing artificial intelligence (AI) was developed. This workflow not only enhances transcription speed but also addresses the issue of AI-generated transcripts often lacking compatibility with standard content analysis software. Within this workflow, automatic speech recognition is employed to create initial transcripts from audio recordings, which are then formatted to be compatible with content analysis software such as ATLAS or MAXQDA. Empirical data from a study of 12 interviews suggests that this workflow can reduce transcription time by up to 76.4%. Furthermore, by using widely used standard software, this process is suitable for both students and researchers while also being adaptable to a variety of learning, teaching, and research environments. It is also particularly beneficial for non-native speakers. In addition, the workflow is GDPR-compliant and facilitates local, offline transcript generation, which is crucial when dealing with sensitive data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13031 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Halving transcription time: A fast, user-friendly and GDPR-compliant workflow to create AI-assisted transcripts for content analysis Sponholz, Jakob Weilinghoff, Andreas Schopf, Juliane Computation and Language In qualitative research, data transcription is often labor-intensive and time-consuming. To expedite this process, a workflow utilizing artificial intelligence (AI) was developed. This workflow not only enhances transcription speed but also addresses the issue of AI-generated transcripts often lacking compatibility with standard content analysis software. Within this workflow, automatic speech recognition is employed to create initial transcripts from audio recordings, which are then formatted to be compatible with content analysis software such as ATLAS or MAXQDA. Empirical data from a study of 12 interviews suggests that this workflow can reduce transcription time by up to 76.4%. Furthermore, by using widely used standard software, this process is suitable for both students and researchers while also being adaptable to a variety of learning, teaching, and research environments. It is also particularly beneficial for non-native speakers. In addition, the workflow is GDPR-compliant and facilitates local, offline transcript generation, which is crucial when dealing with sensitive data. |
| title | Halving transcription time: A fast, user-friendly and GDPR-compliant workflow to create AI-assisted transcripts for content analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.13031 |