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本文引用的文献

1
Articulatory movements during vowels in speakers with dysarthria and healthy controls.构音障碍患者与健康对照者在发元音时的发音动作。
J Speech Lang Hear Res. 2008 Jun;51(3):596-611. doi: 10.1044/1092-4388(2008/043).
2
Acoustic analysis of speech tasks performed by three individuals with spinocerebellar ataxia.对三名脊髓小脑共济失调患者执行言语任务的声学分析。
Folia Phoniatr Logop. 2004 Nov-Dec;56(6):367-80. doi: 10.1159/000081084.
3
Interarticulator coordination in dysarthria: an X-ray microbeam study.构音障碍中的关节间协调:一项X射线微束研究。
J Speech Lang Hear Res. 2003 Oct;46(5):1247-61. doi: 10.1044/1092-4388(2003/097).
4
Toward an acoustic typology of motor speech disorders.迈向运动性言语障碍的声学类型学
Clin Linguist Phon. 2003 Sep;17(6):427-45. doi: 10.1080/0269920031000086248.
5
The effects of familiarization on intelligibility and lexical segmentation in hypokinetic and ataxic dysarthria.熟悉度对运动减退型和共济失调型构音障碍的可懂度及词汇切分的影响。
J Acoust Soc Am. 2002 Dec;112(6):3022-30. doi: 10.1121/1.1515793.
6
Prosodic variation across sampling tasks in normal and dysarthric speakers.正常和构音障碍患者在不同采样任务中的韵律变化。
Logoped Phoniatr Vocol. 2001;26(4):151-64. doi: 10.1080/14015430127772.
7
Quantitative characterizations of speech rhythm: syllable-timing in Singapore English.语音节奏的定量表征:新加坡英语中的音节计时
Lang Speech. 2000 Oct-Dec;43(Pt 4):377-401. doi: 10.1177/00238309000430040301.
8
Lexical boundary error analysis in hypokinetic and ataxic dysarthria.运动减少型和共济失调型构音障碍的词汇边界错误分析
J Acoust Soc Am. 2000 Jun;107(6):3415-24. doi: 10.1121/1.429412.
9
Prevalence and characteristics of dysarthria in a multiple-sclerosis incidence cohort: relation to neurological data.多 sclerosis 发病率队列中构音障碍的患病率及特征:与神经学数据的关系
Folia Phoniatr Logop. 2000 Jul-Aug;52(4):160-77. doi: 10.1159/000021531.
10
Correlates of linguistic rhythm in the speech signal.语音信号中语言节奏的相关因素。
Cognition. 1999 Dec 17;73(3):265-92. doi: 10.1016/s0010-0277(99)00058-x.

量化构音障碍中的言语节奏异常。

Quantifying speech rhythm abnormalities in the dysarthrias.

作者信息

Liss Julie M, White Laurence, Mattys Sven L, Lansford Kaitlin, Lotto Andrew J, Spitzer Stephanie M, Caviness John N

机构信息

Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, USA.

出版信息

J Speech Lang Hear Res. 2009 Oct;52(5):1334-52. doi: 10.1044/1092-4388(2009/08-0208). Epub 2009 Aug 28.

DOI:10.1044/1092-4388(2009/08-0208)
PMID:19717656
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3738185/
Abstract

PURPOSE

In this study, the authors examined whether rhythm metrics capable of distinguishing languages with high and low temporal stress contrast also can distinguish among control and dysarthric speakers of American English with perceptually distinct rhythm patterns. Methods Acoustic measures of vocalic and consonantal segment durations were obtained for speech samples from 55 speakers across 5 groups (hypokinetic, hyperkinetic, flaccid-spastic, ataxic dysarthrias, and controls). Segment durations were used to calculate standard and new rhythm metrics. Discriminant function analyses (DFAs) were used to determine which sets of predictor variables (rhythm metrics) best discriminated between groups (control vs. dysarthrias; and among the 4 dysarthrias). A cross-validation method was used to test the robustness of each original DFA.

RESULTS

The majority of classification functions were more than 80% successful in classifying speakers into their appropriate group. New metrics that combined successive vocalic and consonantal segments emerged as important predictor variables. DFAs pitting each dysarthria group against the combined others resulted in unique constellations of predictor variables that yielded high levels of classification accuracy.

CONCLUSIONS

This study confirms the ability of rhythm metrics to distinguish control speech from dysarthrias and to discriminate dysarthria subtypes. Rhythm metrics show promise for use as a rational and objective clinical tool.

摘要

目的

在本研究中,作者检验了能够区分具有高低时间重音对比的语言的节奏指标,是否也能区分具有明显节奏模式的美国英语正常和构音障碍说话者。方法:获取了5组(运动减退型、运动亢进型、弛缓-痉挛型、共济失调型构音障碍以及正常对照组)55名说话者语音样本中元音和辅音片段时长的声学测量数据。片段时长用于计算标准和新的节奏指标。判别函数分析(DFA)用于确定哪些预测变量集(节奏指标)能最佳区分不同组(正常对照组与构音障碍组;以及4种构音障碍组之间)。采用交叉验证方法来检验每个原始DFA的稳健性。

结果

大多数分类函数将说话者正确分类到相应组的成功率超过80%。结合连续元音和辅音片段的新指标成为重要的预测变量。将每个构音障碍组与其他组联合进行DFA分析,得到了能产生高分类准确率的独特预测变量组合。

结论

本研究证实了节奏指标区分正常语音和构音障碍以及区分构音障碍亚型的能力。节奏指标有望成为一种合理且客观的临床工具。