促进深度声学表型分析：一种作为计算分析、机器学习和临床推理前奏的婴儿发声基础编码方案
Facilitating deep acoustic phenotyping: A basic coding scheme of infant vocalisations preluding computational analysis, machine learning and clinical reasoning
AI总结：
本文提出一种包含五类婴儿发声的可靠、易用编码方案，并通过45名婴儿前4个月的纵向数据验证其一致性，为语言发展研究、机器学习与临床分析奠定基础。
摘要：
理论背景：早期言语发展尚未被充分理解，尤其是其形成阶段。研究问题：能否定义一种可靠且易用的编码方案，用于早期婴儿发声分类，并作为进一步分析语言发展的基础？方法：在一项针对45名神经典型婴儿的纵向研究中，我们分析了其出生后前4个月的发声。音频片段被归入5个类别：（1）浊音发声和（2）清音发声；（3）已定义信号；（4）非目标声音；（5）不可归类声音。结果：两名经验不同的女性编码者在未接受密集训练的情况下取得了高度一致性。讨论与结论：该可靠方案可用于研究和临床环境，以高效编码婴儿发声，并作为详细人工分析和机器分析的基础。
英文摘要：
Theoretical background: early verbal development is not yet fully understood, especially in its formative phase. Research question: can a reliable, easy-to-use coding scheme for the classification of early infant vocalizations be defined that is applicable as a basis for further analysis of language development? Methods: in a longitudinal study of 45 neurotypical infants, we analyzed vocalizations of the first 4 months of life. Audio segments were assigned to 5 classes: (1) Voiced and (2) Voiceless vocalizations; (3) Defined signal; (4) Non-target; (5) Nonassignable. Results: Two female coders with different experience achieved high agreement without intensive training. Discussion and Conclusion: The reliable scheme can be used in research and clinical settings for efficient coding of infant vocalizations, as a basis for detailed manual and machine analyses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
