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Automatic discrimination of emotion from spoken Finnish.

In this paper, experiments on the automatic discrimination of basic emotions from spoken Finnish are described. For the purpose of the study, a large emotional speech corpus of Finnish was collected; 14 professional actors acted as speakers, and simulated four primary emotions when reading out a sem...

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Published in:Language & Speech Vol. 47; no. 4; pp. 383 - 413
Main Authors: Toivanen J, Väyrynen E, Seppänen T
Format: research tables/charts Journal Article
Published: Sage Publications Inc. Dec2004
Online Access:View this record in EBSCOhost
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      dt: Dec2004
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      pub: Sage Publications Inc.
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        atl: Automatic discrimination of emotion from spoken Finnish.
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          Toivanen J
          Väyrynen E
          Seppänen T
        affil: Media Team, Department of Electrical and Information Engineering, Information Processing Laboratory, P.O. BOX 4500, FIN-90014 University of Oulu, Finland; juhani.toivanen@ee.oulu.fi
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          Emotions Finland
          Speech Acoustics
          Speech Perception
          Adolescence
          Adult
          Algorithms Utilization
          Audiorecording
          Descriptive Statistics
          Female
          Finland
          Funding Source
          Male
          Middle Age
          Performing Artists
          Students, Middle School
          Human
          Adolescent: 13-18 years
          Adult: 19-44 years
          Middle Aged: 45-64 years
          Female
          Male
      ab: In this paper, experiments on the automatic discrimination of basic emotions from spoken Finnish are described. For the purpose of the study, a large emotional speech corpus of Finnish was collected; 14 professional actors acted as speakers, and simulated four primary emotions when reading out a semantically neutral text. More than 40 prosodic features were derived and automatically computed from the speech samples. Two application scenarios were tested: the first scenario was speaker-independent for a small domain of speakers while the second scenario was completely speaker-independent. Human listening experiments were conducted to assess the perceptual adequacy of the emotional speech samples. Statistical classification experiments indicated that, with the optimal combination of prosodic feature vectors, automatic emotion discrimination performance close to human emotion recognition ability was achievable.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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