Atkins Marisha Speights, Boyce Suzanne E, MacAuslan Joel, Silbert Noah
Auburn University.
University of Cincinnati.
Proc Int Congr Phon Sci. 2019 Aug;2019:1054-1058.
A common indicator of speech production disorders in children is a reduced ability to articulate complex syllables. Clinical studies of syllabic complexity of child speech have traditionally relied on phonetic transcription by trained listeners to characterize deviations in phonotatic structure. The labor-intensive nature of transcribing, segmenting, labeling, and hand-counting syllables has limited clinical analysis of large samples of continuous speech. In this paper, we discuss the use of a computer-assisted method, Automatic Syllabic Cluster Analysis, for broad transcription, segmentation, and counting syllabic units as a means for fast analysis of differences in speech precision when comparing children with and without speech-related disorders. Findings show that the number of syllabic clusters per utterance is a significant indicator of speech disorder.
儿童言语产生障碍的一个常见指标是清晰发出复杂音节的能力下降。传统上，关于儿童言语音节复杂性的临床研究依赖于训练有素的听众进行语音转录，以描述音系结构的偏差。转录、分割、标注和手工计数音节的劳动密集型性质限制了对大量连续言语样本的临床分析。在本文中，我们讨论了一种计算机辅助方法——自动音节聚类分析的使用，用于广泛转录、分割和计数音节单元，作为比较有和没有言语相关障碍的儿童时快速分析言语准确性差异的一种手段。研究结果表明，每个话语中的音节聚类数量是言语障碍的一个重要指标。
