Automated Detection of Voice in News Text – Evaluating Tools for Reported Speech and Speaker Recognition
- Author(s)
- Ahrabhi Kathirgamalingam, Fabienne Lind, Hajo Boomgaarden
- Abstract
The automated content analysis of text has become integral to contemporary communication and journalism research. However, automated approaches are seldom utilized to analyze reported voice in text, while doing so would offer valuable insights into media and communication practices. Bridging the fields of communication science and computational linguistics, this study reviews and evaluates off-the-shelf tools for automated voice detection (of direct/indirect speech and of speakers) with respect to user experience and validity. Manually annotated English news articles and Twitter data served as baseline for evaluating the automated detection of voice. Findings indicate that the tools being assessed offer a satisfactory user experience and provide promising solutions for detecting direct speech automatically, encouraging fellow researchers to utilize automated detection for direct quotations. However, the recognition of indirect speech and speakers needs considerable improvement.
- Organisation(s)
- Department of Communication
- Journal
- Computational Communication Research
- Volume
- 5
- Pages
- 85-108
- No. of pages
- 24
- ISSN
- 2665-9085
- DOI
- https://doi.org/10.5117/CCR2023.1.004.KATH
- Publication date
- 01-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 508008 Media analysis
- Keywords
- ASJC Scopus subject areas
- Computational Theory and Mathematics, Linguistics and Language
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/automated-detection-of-voice-in-news-text--evaluating-tools-for-reported-speech-and-speaker-recognition(b3a3f997-ff2b-46d0-99bf-6d909a692d10).html