Automated News Reading in the Neural Age: Audience Reception and Perceived Credibility of a News Broadcast Read By a Neural Voice

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Abstract

Automated journalism is rapidly developing in the news industry. Among the most recent and promising technological potentials are neural voices, i.e., text-to-speech technology powered by neural networks. Based on a reception analysis with in-depth qualitative interviews (N = 12), this study explores how Danish radio listeners receive a full news broadcast read by a neural voice and perceive the credibility of the neural reader and the news content. Results show that the participants divide into two types: the perspicacious listeners who realize or suspect that the news reading is artificially synthesized and, to some degree, are annoyed by it, and the oblivious listeners who believe the news is read by a human and are predominantly positive towards it. Participants from both groups pay particular attention to voice emotionality when evaluating the appropriateness of the neural news reader. Also, they tend to attribute human characteristics to the neural news reader. The participants single out the news messages as well as the media organization behind the news broadcast, rather than the neural voice itself as critical components constituting credibility. Transparency is of great importance when applying a neural voice in a news broadcast, since it is a prerequisite for credibility.

Original languageEnglish
JournalJournalism Studies
Volume23
Issue number8
Pages (from-to)896-914
ISSN1461-670X
DOIs
Publication statusPublished - Feb 2022

Keywords

  • Eliza effect
  • Text-to-speech (TTS)
  • anthropomorphism
  • automated journalism
  • credibility
  • neural voices

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