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The children's emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language.

Elena LyaksoNersisson RubanOlga FrolovaMary A Mekala
Published in: PloS one (2023)
The current study investigated the features of cross-cultural recognition of four basic emotions "joy-neutral (calm state)-sad-anger" in the spontaneous and acting speech of Indian and Russian children aged 8-12 years across Russian and Tamil languages. The research tasks were to examine the ability of Russian and Indian experts to recognize the state of Russian and Indian children by their speech, determine the acoustic features of correctly recognized speech samples, and specify the influence of the expert's language on the cross-cultural recognition of the emotional states of children. The study includes a perceptual auditory study by listeners and instrumental spectrographic analysis of child speech. Different accuracy and agreement between Russian and Indian experts were shown in recognizing the emotional states of Indian and Russian children by their speech, with more accurate recognition of the emotional state of children in their native language, in acting speech vs spontaneous speech. Both groups of experts recognize the state of anger via acting speech with the high agreement. The difference between the groups of experts was in the definition of joy, sadness, and neutral states depending on the test material with a different agreement. Speech signals with emphasized differences in acoustic patterns were more accurately classified by experts as belonging to emotions of different activation. The data showed that, despite the universality of basic emotions, on the one hand, the cultural environment affects their expression and perception, on the other hand, there are universal non-linguistic acoustic features of the voice that allow us to identify emotions via speech.
Keyphrases
  • young adults
  • hearing loss
  • autism spectrum disorder
  • high resolution
  • artificial intelligence
  • clinical practice
  • big data
  • deep learning