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.

中文翻译：

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在典型和非典型发展中有效收集和表达前言数据

人类的言语发展是指直到孩子的第一个有意义的单词出现之前，声音能力不断提高的时期。在过去的几十年中，研究集中于典型发展中的前言行为。语音前的声音模式已经过语音分类和声学表征。最近，讨论了特定的前言现象，以作为非典型发展的早期指标。音频信号处理和机器学习的最新进展已为言语行为分析提供了新颖的方法，包括基于自动发声的典型和非典型发育个体的区分。在本文中，我们从方法上概述了当前用于智能音频分析范例的收集和声学表示前言数据的策略。讨论了数据收集和数据表示中的效率。遵循当前的研究趋势，我们特别关注处理具有较晚发现的发育障碍（例如自闭症谱系障碍或Rett综合征）的人的前言数据时出现的挑战。