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Efficient Collection and Representation of Preverbal Data in Typical and Atypical Development.
Pokorny, Florian B; Bartl-Pokorny, Katrin D; Zhang, Dajie; Marschik, Peter B; Schuller, Dagmar; Schuller, Björn W.
Afiliación
  • Pokorny FB; iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria.
  • Bartl-Pokorny KD; Machine Intelligence & Signal Processing group (MISP), Chair of Human-Machine Communication, Technical University of Munich, Munich, Germany.
  • Zhang D; iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria.
  • Marschik PB; iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria.
  • Schuller D; Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany.
  • Schuller BW; Leibniz ScienceCampus Primate Cognition, Göttingen, Germany.
J Nonverbal Behav ; 44(4): 419-436, 2020.
Article en En | MEDLINE | ID: mdl-33088008
Human preverbal development refers to the period of steadily increasing vocal capacities until the emergence of a child's first meaningful words. Over the last decades, research has intensively focused on preverbal behavior in typical development. Preverbal vocal patterns have been phonetically classified and acoustically characterized. More recently, specific preverbal phenomena were discussed to play a role as early indicators of atypical development. Recent advancements in audio signal processing and machine learning have allowed for novel approaches in preverbal behavior analysis including automatic vocalization-based differentiation of typically and atypically developing individuals. In this paper, we give a methodological overview of current strategies for collecting and acoustically representing preverbal data for intelligent audio analysis paradigms. Efficiency in the context of data collection and data representation is discussed. Following current research trends, we set a special focus on challenges that arise when dealing with preverbal data of individuals with late detected developmental disorders, such as autism spectrum disorder or Rett syndrome.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Nonverbal Behav Año: 2020 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Nonverbal Behav Año: 2020 Tipo del documento: Article País de afiliación: Austria Pais de publicación: Estados Unidos