We researched how "likable" or "pleasant" a speaker appears based on a subset of the "Agender" database which was recently introduced at the 2010 Interspeech Paralinguistic Challenge. 32 participants rated the stimuli according to their likability on a seven point scale. An Anova showed that the samples rated are significantly different although the inter-rater agreement is not very high. Experiments with automatic regression and classification by REPTree ensemble learning resulted in a cross-correlation of up to .378 with the evaluator weighted estimator, and 67.6% accuracy in binary classification (likable / not likable). Analysis of individual acoustic feature groups reveals that for this data, auditory spectral features seem to contribute the most to reliable automatic likability analysis.
Bibliographic reference. Burkhardt, Felix / Schuller, Björn / Weiss, Benjamin / Weninger, Felix (2011): "“would you buy a car from me?” - on the likability of telephone voices", In INTERSPEECH-2011, 1557-1560.