Academic Scholarship

Voice attractiveness: Influence of stimulus type and duration

Behavioral Research Methods, 45, 405-413, December 2013

Photo Needed Sona M. Patel, Ph.D.
Department of Speech-Language Pathology
Dr. Camille Ferdenzi, Dr. Inez Mehu-Blantar, Merriam Khidasheli, Dr. David Sander, Dr. Sylvain Delplanque

Voice attractiveness is a relatively new area of research. Some aspects of the methodology used in this domain deserve particular attention. Especially, the duration of voice samples is often neglected as a factor and happens to be manipulated without the perceptual consequences of these manipulations being known. Moreover, the type of voice stimulus varies from a single vowel to complex sentences.

The aim of this experiment was to investigate the extent to which stimulus duration (nonmanipulated vs. normalized) and type (vowel vs. word) influence perceived voice attractiveness. Twenty-seven male and female raters made attractiveness judgments of 30 male and female voice samples. Voice samples included a single vowel /a/, a three-vowel series /i a o/, and the French word “bonjour” (i.e., “hello”). These samples were presented in three conditions: nonmanipulated, shortened, and lengthened duration. Duration manipulation was performed using the pitch synchronous overlap and add (PSOLA) algorithm implemented in Praat. 

Results for the effect of stimulus type showed that word length samples were more attractive to the opposite sex than vowels. Results for the effect of duration showed that the nonmanipulated sound sample duration was not predictive of perceived attractiveness. Duration manipulation, on the other hand, altered perceived attractiveness for the lengthening condition. In particular, there was a linear decrease in attractiveness as a function of modification percentage (especially for the word, as compared with the vowels). Recommendations for voice sample normalization with the PSOLA algorithm are thus to prefer shortening over lengthening and, if not possible, to limit the extent of duration manipulation—for example, by normalizing to the mean sample duration.


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