Marisha Speights Atkins, Suzanne E Boyce, Joel MacAuslan, Noah Silbert
{"title":"计算机辅助连续语音音节复杂性分析作为儿童语言障碍的衡量标准。","authors":"Marisha Speights Atkins, Suzanne E Boyce, Joel MacAuslan, Noah Silbert","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74531,"journal":{"name":"Proceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences","volume":"2019 ","pages":"1054-1058"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520931/pdf/","citationCount":"0","resultStr":"{\"title\":\"Computer-Assisted Syllable Complexity Analysis of Continuous Speech as a Measure of Child Speech Disorders.\",\"authors\":\"Marisha Speights Atkins, Suzanne E Boyce, Joel MacAuslan, Noah Silbert\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":74531,\"journal\":{\"name\":\"Proceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences\",\"volume\":\"2019 \",\"pages\":\"1054-1058\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520931/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computer-Assisted Syllable Complexity Analysis of Continuous Speech as a Measure of Child Speech Disorders.
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.