Back to search

Article

Breaking voice identity perception: Expressive voices are more confusable for listeners

2018-05-23

Abstract excerpt

<p>The human voice is a highly flexible instrument for self-expression, yet voice identity perception is largely studied using controlled speech recordings. Using two "voice sorting" tasks with naturally-varying stimuli, we compared the performance of listeners who were familiar and unfamiliar with the TV show Breaking Bad: listeners organized audio clips of speech with 1) low and 2) high expressiveness into perce...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
eb39b68e-9ae4-5082-ad9b-3d87caac1946
DOI
10.31234/osf.io/mq587
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Breaking voice identity perception: Expressive voices are more confusable for listenersDOI 10.31234/osf.io/mq587
Effects of Self-similarity and Self-Generation on the Perceptual Prioritisation of VoicesSemantic proximity
Prosodic cues strengthen human-AI voice boundaries: Listeners do not easily perceive human speakers and AI clones as the same personSemantic proximity
Auditory Perceptual Assessment of Voices: Examining Perceptual Ratings as a Function of Voice ExperienceSemantic proximity
Perception and Social Evaluation of Cloned and Recorded Voices: Effects of Familiarity and Self-Relevance.Semantic proximity
Human brains implicitly and rapidly distinguish AI from human voices before decoding prosodic meaningSemantic proximity
Validation of Scrambling Methods for Vocal Affect BurstsSemantic proximity
Context-dependent acoustic dimensions underlying voice discrimination: Evidence from interpretable machine learning modeling of human perceptionSemantic proximity
Context-dependent acoustic dimensions underlying voice discrimination: Evidence from interpretable machine learning modeling of human perceptionSemantic proximity
Voice clones sound realistic but not (yet) hyperrealisticSemantic proximity
Voice clones sound realistic but not (yet) hyperrealisticSemantic proximity
From Voice to Self: An Integrative Framework on Self-Voice ProcessingSemantic proximity
From Voice to Self: An Integrative Framework on Self-Voice ProcessingSemantic proximity
Select a neighboring publication to make it the new centre.