Confides: A Visual Analytics Solution for Automated Speech Recognition Analysis and Exploration
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913444461019136 |
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| author | Ha, Sunwoo Lim, Chaehun Crouser, R. Jordan Ottley, Alvitta |
| author_facet | Ha, Sunwoo Lim, Chaehun Crouser, R. Jordan Ottley, Alvitta |
| contents | Confidence scores of automatic speech recognition (ASR) outputs are often inadequately communicated, preventing its seamless integration into analytical workflows. In this paper, we introduce ConFides, a visual analytic system developed in collaboration with intelligence analysts to address this issue. ConFides aims to aid exploration and post-AI-transcription editing by visually representing the confidence associated with the transcription. We demonstrate how our tool can assist intelligence analysts who use ASR outputs in their analytical and exploratory tasks and how it can help mitigate misinterpretation of crucial information. We also discuss opportunities for improving textual data cleaning and model transparency for human-machine collaboration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00223 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Confides: A Visual Analytics Solution for Automated Speech Recognition Analysis and Exploration Ha, Sunwoo Lim, Chaehun Crouser, R. Jordan Ottley, Alvitta Human-Computer Interaction Confidence scores of automatic speech recognition (ASR) outputs are often inadequately communicated, preventing its seamless integration into analytical workflows. In this paper, we introduce ConFides, a visual analytic system developed in collaboration with intelligence analysts to address this issue. ConFides aims to aid exploration and post-AI-transcription editing by visually representing the confidence associated with the transcription. We demonstrate how our tool can assist intelligence analysts who use ASR outputs in their analytical and exploratory tasks and how it can help mitigate misinterpretation of crucial information. We also discuss opportunities for improving textual data cleaning and model transparency for human-machine collaboration. |
| title | Confides: A Visual Analytics Solution for Automated Speech Recognition Analysis and Exploration |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.00223 |