• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

任务相关性的自动声学分析在痉挛性发声障碍与肌肉紧张性发声障碍中的应用。

Automated acoustic analysis of task dependency in adductor spasmodic dysphonia versus muscle tension dysphonia.

机构信息

Department of Communication Sciences and Disorders, The University of Utah, Salt Lake City, Utah, U.S.A.

出版信息

Laryngoscope. 2014 Mar;124(3):718-24. doi: 10.1002/lary.24362. Epub 2013 Oct 1.

DOI:10.1002/lary.24362
PMID:23946147
Abstract

OBJECTIVES/HYPOTHESIS: Distinguishing muscle tension dysphonia (MTD) from adductor spasmodic dysphonia (ADSD) can be difficult. Unlike MTD, ADSD is described as "task-dependent," implying that dysphonia severity varies depending upon the demands of the vocal task, with connected speech thought to be more symptomatic than sustained vowels. This study used an acoustic index of dysphonia severity (i.e., the Cepstral Spectral Index of Dysphonia [CSID]) to: 1) assess the value of "task dependency" to distinguish ADSD from MTD, and to 2) examine associations between the CSID and listener ratings.

STUDY DESIGN

Case-Control Study.

METHODS

CSID estimates of dysphonia severity for connected speech and sustained vowels of patients with ADSD (n = 36) and MTD (n = 45) were compared. The diagnostic precision of task dependency (as evidenced by differences in CSID-estimated dysphonia severity between connected speech and sustained vowels) was examined.

RESULTS

In ADSD, CSID-estimated severity for connected speech (M = 39. 2, SD = 22.0) was significantly worse than for sustained vowels (M = 29.3, SD = 21.9), [P = .020]. Whereas in MTD, no significant difference in CSID-estimated severity was observed between connected speech (M = 55.1, SD = 23.8) and sustained vowels (M = 50.0, SD = 27.4), [P = .177]. CSID evidence of task dependency correctly identified 66.7% of ADSD cases (sensitivity) and 64.4% of MTD cases (specificity). CSID and listener ratings were significantly correlated.

CONCLUSION

Task dependency in ADSD, as revealed by differences in acoustically-derived estimates of dysphonia severity between connected speech and sustained vowel production, is a potentially valuable diagnostic marker.

摘要

目的/假设:鉴别肌肉紧张性发声障碍(MTD)和内收性痉挛性发声障碍(ADSD)可能具有一定难度。与 MTD 不同,ADSD 被描述为“任务依赖型”,这意味着发音障碍的严重程度取决于发声任务的要求,与连续言语相比,发元音时的症状更为明显。本研究使用一种发音障碍严重程度的声学指标(即,浊音频谱不谐指数[CSID])来:1)评估“任务依赖性”鉴别 ADSD 和 MTD 的价值,以及 2)检查 CSID 与听障者评估之间的关联。

研究设计

病例对照研究。

方法

比较 ADSD(n = 36)和 MTD(n = 45)患者的连续言语和元音的 CSID 估计的发音障碍严重程度。检查任务依赖性的诊断精度(表现为连续言语和元音的 CSID 估计的发音障碍严重程度之间的差异)。

结果

在 ADSD 中,连续言语的 CSID 估计严重程度(M = 39.2,SD = 22.0)明显差于元音(M = 29.3,SD = 21.9),[P = .020]。而在 MTD 中,连续言语(M = 55.1,SD = 23.8)和元音(M = 50.0,SD = 27.4)的 CSID 估计严重程度无显著差异,[P = .177]。CSID 证据表明,任务依赖性正确识别了 66.7%的 ADSD 病例(敏感性)和 64.4%的 MTD 病例(特异性)。CSID 和听障者评估之间存在显著相关性。

结论

ADSD 中的任务依赖性,表现为连续言语和元音产生时发音障碍严重程度的声学差异,可能是一种有价值的诊断标志物。

相似文献

1
Automated acoustic analysis of task dependency in adductor spasmodic dysphonia versus muscle tension dysphonia.任务相关性的自动声学分析在痉挛性发声障碍与肌肉紧张性发声障碍中的应用。
Laryngoscope. 2014 Mar;124(3):718-24. doi: 10.1002/lary.24362. Epub 2013 Oct 1.
2
Task specificity in adductor spasmodic dysphonia versus muscle tension dysphonia.内收型痉挛性发声障碍与肌肉紧张性发声障碍的任务特异性
Laryngoscope. 2005 Feb;115(2):311-6. doi: 10.1097/01.mlg.0000154739.48314.ee.
3
Toward validation of the cepstral spectral index of dysphonia (CSID) as an objective treatment outcomes measure.探讨复声谱指数(CSID)作为一种客观的治疗效果评估指标的有效性。
J Voice. 2013 Jul;27(4):401-10. doi: 10.1016/j.jvoice.2013.04.002.
4
Comparison of Two Multiparameter Acoustic Indices of Dysphonia Severity: The Acoustic Voice Quality Index and Cepstral Spectral Index of Dysphonia.两种嗓音障碍严重程度多参数声学指标的比较:嗓音声学质量指数和嗓音障碍的谐波倒谱谱指数
J Voice. 2018 Jul;32(4):515.e1-515.e13. doi: 10.1016/j.jvoice.2017.06.012. Epub 2017 Jul 21.
5
Toward improved differential diagnosis of adductor spasmodic dysphonia and muscle tension dysphonia.迈向改善内收肌痉挛性发音障碍和肌张力障碍性发音障碍的鉴别诊断
Folia Phoniatr Logop. 2007;59(2):83-90. doi: 10.1159/000098341.
6
Adductor spasmodic dysphonia versus muscle tension dysphonia: examining the diagnostic value of recurrent laryngeal nerve lidocaine block.内收肌痉挛性发音障碍与肌肉紧张性发音障碍:探讨喉返神经利多卡因阻滞的诊断价值。
Ann Otol Rhinol Laryngol. 2007 Mar;116(3):161-8. doi: 10.1177/000348940711600301.
7
Differentiation of adductor-type spasmodic dysphonia from muscle tension dysphonia by spectral analysis.通过频谱分析鉴别内收型痉挛性发声障碍与肌张力障碍性发声障碍。
Otolaryngol Head Neck Surg. 2007 Oct;137(4):576-81. doi: 10.1016/j.otohns.2007.03.040.
8
Differential diagnosis of muscle tension dysphonia and adductor spasmodic dysphonia using spectral moments of the long-term average spectrum.使用长时平均谱的谱矩对肌肉紧张性发音障碍和内收性痉挛性发音障碍进行鉴别诊断。
Laryngoscope. 2010 Apr;120(4):749-57. doi: 10.1002/lary.20741.
9
Differential diagnosis of adductor spasmodic dysphonia and muscle tension dysphonia using phonatory break analysis.运用发声中断分析对内收肌痉挛性发音障碍和肌肉紧张性发音障碍进行鉴别诊断。
Laryngoscope. 2008 Dec;118(12):2245-53. doi: 10.1097/MLG.0b013e318184577c.
10
Toward improved ecological validity in the acoustic measurement of overall voice quality: combining continuous speech and sustained vowels.提高整体语音质量声学测量的生态有效性:结合连续语音和持续元音。
J Voice. 2010 Sep;24(5):540-55. doi: 10.1016/j.jvoice.2008.12.014. Epub 2009 Nov 2.

引用本文的文献

1
Supraglottic Laryngeal Maneuvers in Adductor Laryngeal Dystonia During Connected Speech.连串言语期间内收性喉肌张力障碍中的声门上喉部手法
J Voice. 2024 Aug 30. doi: 10.1016/j.jvoice.2024.08.009.
2
Deep Learning-Based Analysis of Glottal Attack and Offset Times in Adductor Laryngeal Dystonia.基于深度学习的内收型喉肌张力障碍中声门起音和终止时间分析
J Voice. 2023 Nov 15. doi: 10.1016/j.jvoice.2023.10.011.
3
Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters.基于声学和感知参数的痉挛性发声障碍的机器学习评估
Bioengineering (Basel). 2023 Mar 28;10(4):426. doi: 10.3390/bioengineering10040426.
4
Automated Creak Differentiates Adductor Laryngeal Dystonia and Muscle Tension Dysphonia.自动咔哒声可区分内收性喉痉挛和肌肉紧张性发音障碍。
Laryngoscope. 2023 Oct;133(10):2687-2694. doi: 10.1002/lary.30588. Epub 2023 Jan 30.
5
Symptom Expression Across Voiced Speech Sounds in Adductor Laryngeal Dystonia.内收型喉肌张力障碍中浊语音的症状表现
J Voice. 2025 Mar;39(2):567.e23-567.e30. doi: 10.1016/j.jvoice.2022.10.002. Epub 2022 Nov 21.
6
Spectral Aggregate of the High-Passed Fundamental Frequency and Its Relationship to the Primary Acoustic Features of Adductor Laryngeal Dystonia.基频带通后的频谱总和及其与收束性喉肌张力障碍主要声学特征的关系。
J Speech Lang Hear Res. 2022 Nov 17;65(11):4085-4095. doi: 10.1044/2022_JSLHR-22-00157. Epub 2022 Oct 5.
7
Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy.高速视频内镜检查中内收型痉挛性发声障碍患者连贯言语时基于深度学习的声带动力学表现
J Voice. 2025 Mar;39(2):570.e1-570.e15. doi: 10.1016/j.jvoice.2022.08.022. Epub 2022 Sep 23.
8
Correlating Perceptual Voice Quality in Adductor Spasmodic Dysphonia With Computer Vision Assessment of Glottal Geometry Dynamics.关联评估喉肌痉挛性发音障碍的计算机视觉与感知声音质量的评估。
J Speech Lang Hear Res. 2022 Oct 17;65(10):3695-3708. doi: 10.1044/2022_JSLHR-22-00053. Epub 2022 Sep 21.
9
Detection of Vocal Fold Image Obstructions in High-Speed Videoendoscopy During Connected Speech in Adductor Spasmodic Dysphonia: A Convolutional Neural Networks Approach.基于卷积神经网络的痉挛性发声障碍患者连接性言语时高速视频内镜下声带图像遮挡的检测。
J Voice. 2024 Jul;38(4):951-962. doi: 10.1016/j.jvoice.2022.01.028. Epub 2022 Mar 16.
10
Acoustic Model of Perceived Overall Severity of Dysphonia in Adductor-Type Laryngeal Dystonia.内收型喉肌张力障碍中嗓音障碍总体严重程度的声学模型
J Speech Lang Hear Res. 2020 Aug 10;63(8):2713-2722. doi: 10.1044/2020_JSLHR-19-00354. Epub 2020 Jul 16.