Investigation of Development of Phonology using Artificial Intelligence: A Preliminary Study

Vijay Kumar, Anjani Jain, Jitendra Kumar Saini, Leena Bhardwaj
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Abstract

Background: Investigation of the development of acquisition of phonological processes is a very important phenomenon to predict language development in childhood. Phonological development refers to the gradual acquisition of an adult-like speech system used to convey meaning in a language. Adequate phonological development can lead to higher speech intelligibility, essential for perception and speech production. The test battery approach for phonological assessment includes formal as well as informal testing modalities. Speech intelligibility is a vital parameter for the assessment of phonological development. This is done via scoring collected audio speech samples, followed by IPA transcription and scoring and analysis of data manually. The primary objective of the study was to investigate the speech intelligibility score across real-timed stimuli (rhyming, storytelling, open-ended questions, and picture description tasks) across two age categories, i.e., 3-4 and 4-5 years among 40 Hindi-speaking children. Methods: A total of 40 children (15 girls and 25 boys) in the age range of 3 to 5 years were randomly selected from a junior high school in New Delhi, India. Groupwise speech intelligibility score was calculated for rhyming, storytelling, open-ended questions, and picture description tasks. Results: It was observed that the speech intelligibility score for open-ended questions was significantly higher in the 4-5 years group (94.09 ± 3.7) than the 3-4 years (89.83 ± 8.2), t (1, 38) =2.12, p =0.04). However, no significant improvement in rhyming task [t (1, 38) =1.14, p =0.18), storytelling [t (1, 38) =1.81, p =0.07), and picture description [t (1, 38) =1.89, p =0.06) was observed. Conclusion: Chances of error in conventional method of phonology assessment still persists which may be controlled using Artificial Intelligence (AI). The target phonetic stimulus can be customized based on the linguistic experience and environment.
利用人工智能对音韵学发展的调查:初步研究
背景:语音过程习得的发展是预测儿童语言发展的一个重要现象。语音发展是指逐渐获得一种类似成人的语言系统,用来表达语言的意思。充分的语音发展可以导致更高的语音清晰度,这对感知和语音产生至关重要。语音评估的测试单元方法包括正式和非正式的测试方式。语音可理解度是评估语音发展的重要参数。这是通过对收集到的音频语音样本进行评分,然后手动进行国际音标转录和数据评分和分析来完成的。本研究的主要目的是调查40名印地语儿童在实时刺激(押韵、讲故事、开放式问题和图片描述任务)下的语音清晰度得分,这些语音清晰度得分跨越两个年龄类别,即3-4岁和4-5岁。方法:在印度新德里一所初中随机抽取年龄在3 ~ 5岁的儿童40名,其中女孩15名,男孩25名。计算了押韵、讲故事、开放式问题和图片描述任务的群体语音清晰度得分。结果:4-5岁组开放式问题语音清晰度得分(94.09±3.7)明显高于3-4岁组(89.83±8.2),t (1,38) =2.12, p =0.04)。然而,在押韵任务[t (1,38) =1.14, p =0.18]、讲故事任务[t (1,38) =1.81, p =0.07]和图片描述任务[t (1,38) =1.89, p =0.06]方面没有显著改善。结论:传统的音系评估方法仍然存在误差,可以通过人工智能(AI)加以控制。目标语音刺激可以根据语言经验和环境进行定制。
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来源期刊
Current Materials Science
Current Materials Science Materials Science-Materials Science (all)
CiteScore
0.80
自引率
0.00%
发文量
38
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