PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 25, 2025Trends in Cognitive Sciences23 citationsOpen Access

Understanding voice naturalness

View Full Paper
CNChristine NussbaumSFSascha FrühholzSSStefan R. Schweinberger

Key Points

  • The review aims to systematically define and understand the concept of voice naturalness and its implications for both human and artificial interactions.
  • Analysis of interdisciplinary literature on voice naturalness
  • Comparison of human and synthetic voice studies
  • Development of a conceptual framework based on empirical findings
  • Identification of theoretical models related to voice perception
  • Provides a concise definition of voice naturalness
  • Proposes a new conceptual framework for better understanding
  • Identifies significant gaps in the current literature on voice naturalness
  • Suggests directions for future empirical research

Abstract

The perceived naturalness of a voice is a prominent property emerging from vocal sounds, which affects our interaction with both human and artificial agents. Despite its importance, a systematic understanding of voice naturalness is elusive. This is due to (i) conceptual underspecification, (ii) heterogeneous operationalization, (iii) lack of exchange between research on human and synthetic voices, and (iv) insufficient anchoring in voice perception theory. This review reflects on current insights into voice naturalness by pooling evidence from a wider interdisciplinary literature. Against that backdrop, it offers a concise definition of naturalness and proposes a conceptual framework rooted in both empirical findings and theoretical models. Finally, it identifies gaps in current understanding of voice naturalness and sketches perspectives for empirical progress.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Nussbaum et al. (2025) studied this question.

synapsesocial.com/papers/6985d466c55aa016525d0eeehttps://doi.org/10.1016/j.tics.2025.01.010
Ask AI
Helpful
Bookmark
Share
View Full Paper