How Anxiety State Influences Speech Parameters: A Network Analysis Study from a Real Stressed Scenario.

Abstract

Background/Objectives: Voice analysis has shown promise in anxiety assessment, yet traditional approaches examining isolated acoustic features yield inconsistent results. This study aimed to explore the relationship between anxiety states and vocal parameters from a network perspective in ecologically valid settings. Methods: A cross-sectional study was conducted with 316 undergraduate students (191 males, 125 females; mean age 20.3 ± 0.85 years) who completed a standardized picture description task while their speech was recorded. Participants were categorized into low-anxiety (n = 119) and high-anxiety (n = 197) groups based on self-reported anxiety ratings. Five acoustic parameters-jitter, fundamental frequency (F0), formant frequencies (F1/F2), intensity, and speech rate-were analyzed using network analysis. Results: Network analysis revealed a robust negative relationship between jitter and state anxiety, with jitter as the sole speech parameter consistently linked to state anxiety in the total group. Additionally, higher anxiety levels were associated with a coupling between intensity and F1/F2, whereas the low-anxiety network displayed a sparser organization without intensity and F1/F2 connection. Conclusions: Anxiety could be recognized by speech parameter networks in ecological settings. The distinct pattern with the negative jitter-anxiety relationship in the total network and the connection between intensity and F1/2 in high-anxiety states suggest potential speech markers for anxiety assessment. These findings suggest that state anxiety may directly influence jitter and fundamentally restructure the relationships among speech features, highlighting the importance of examining jitter and speech parameter interactions rather than isolated values in speech detection of anxiety.