Efficient Collection and Representation of Preverbal Data in Typical and Atypical Development

Florian B. Pokorny, Katrin D. Bartl-Pokorny, Dajie Zhang, Peter B. Marschik, Dagmar Schuller, Björn W. Schuller

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)419-436
Number of pages18
JournalJournal of Nonverbal Behavior
Volume44
Issue number4
DOIs
StatePublished - 1 Dec 2020
Externally publishedYes

Keywords

  • Data collection
  • Data representation
  • Developmental disorders
  • Infancy
  • Intelligent audio analysis
  • Preverbal development

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