Favorable stimulus words for the treatment of phonological disorders involving the production of simple onset sounds

ABSTRACT Purpose: to develop and analyze a list of target words providing a favorable environment for the acquisition of target sounds in simple onset position, using a programming algorithm to assign a favorability score for each of these words. Methods: an algorithm was programmed to calculate a favorability score for target words by adding up the weights assigned to the following variables, with regards to the target sound: position within the word, stress pattern, number of syllables, preceding and following context. The algorithm was programmed using Java. The lists of target words for each target sound contained a total of 748 words. A score was calculated for each item, and used to classify the word as favorable, neutral, or unfavorable. Results: target words with the highest scores on the aforementioned variables were considered favorable, as they provided a facilitating context for the production of the target sound. However, target words did not need to meet all aforementioned linguistic criteria to be considered favorable. Conclusion: the algorithm was efficient in calculating favorability scores for the target words. However, although words classified as favorable, or those with high scores on linguistic measures, should be preferred during the selection of target stimuli, these may not necessarily apply to all types of phonological disorder.

摘要：本研究旨在开发并分析一套目标词汇清单，该清单可为简单音节首位置（simple onset position）的目标语音（target sound）习得营造有利环境，并通过编程算法为每个词汇赋予好感度评分（favorability score）。
研究方法：针对目标语音，算法通过累加以下变量的权重值计算目标词汇的好感度评分：目标语音在词汇中的位置、重音模式、音节数量、前后语境。本算法采用Java语言编写。针对各目标语音的目标词汇清单总计包含748个词汇。为每个词汇计算评分后，将其划分为好感、中性、反感三类。
研究结果：在上述变量中得分最高的目标词汇被归类为好感类，因其可为目标语音的产出提供促进性语境。但目标词汇无需满足全部上述语言学标准即可被归入好感类别。
研究结论：本算法可高效计算目标词汇的好感度评分。尽管在选取目标刺激物时，应优先选用被归类为好感类或在语言学指标上得分较高的词汇，但此类词汇未必适用于所有类型的语音障碍（phonological disorder）。