Personality in Speech: Assessment and Automatic by Tim Polzehl

By Tim Polzehl

This paintings combines interdisciplinary wisdom and event from study fields of psychology, linguistics, audio-processing, computer studying, and laptop technology. The paintings systematically explores a unique examine subject dedicated to automatic modeling of character expression from speech. For this objective, it introduces a unique character overview questionnaire and provides the result of wide labeling classes to annotate the speech information with character exams. It presents estimates of the massive five character characteristics, i.e. openness, conscientiousness, extroversion, agreeableness, and
neuroticism. according to a database outfitted at the questionnaire, the e-book offers versions to differentiate varied character forms or periods from speech automatically.

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2012) another extension of the database comprising speech from over 60 naive speakers is introduced and compared to a database of French language introduced by Mohammadi et al. (2010). 6 using various acoustic features. The recorded data is part of the presented multi-speaker collection. More details regarding all three subsets and further extensions towards the presented work are given in Sect. 3. Major difference in between the isolated publication and the presented comprehensive work exist.

However, results showed moderate correlations between intensity and aggressiveness, as well as between flow of speech and clearness and nervousness. Moore (1939) reports on correlations between introversion and lower dominance and a breathy voice in a related study. Mallory and Miller (1958) found correlations between dominance and perceptive loudness, resonance and low pitch. Addington (1968) trained two male and two female speakers to manipulate their speech delivery in terms of seven voice qualities: breathy, tense, thin, flat, throaty, nasal, “orotund”,3 while reading a text passage.

All personalities were performed by a single actor, who has been invited to recording sessions over several weeks. As a result, only very few text- and timedependencies were observed. Applying a cluster analysis the authors give fist insights into similarities and oppositions inherent in the personality assessments of different traits. Again the introduced dataset from Polzehl et al. (2011a) are included into the present collection and will be referred to as text-independent recordings. In Polzehl et al.

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