Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Nocturnal Features and Daytime Characteristics in Narcolepsy: Reliability and Diagnostic Relevance for NT1 vs NT2.

Sleep·2026
Same author

Exercise as a regulator of glymphatic function.

Trends in neurosciences·2026
Same author

Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.

European respiratory review : an official journal of the European Respiratory Society·2026
Same author

Acute and longitudinal magnetic resonance imaging abnormalities in antibody-mediated encephalitis.

Brain communications·2026
Same author

Quantitative susceptibility mapping demonstrates ageing-related long-term structural brain changes in adult rats after mild ischaemic stroke.

Brain communications·2026
Same author

A novel, wearable, in-ear EEG technology to assess sleep and daytime sleepiness.

Bioelectronic medicine·2026

相关实验视频

Updated: Jul 7, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K

走向可解释的语音生物标志物:探索MFCCs

Brian Tracey1, Dmitri Volfson2, James Glass3

  • 1Takeda Pharamaceuticals, Data Science Institute, Cambridge, MA, 02142, USA. brian.tracey@takeda.com.

Scientific reports
|December 20, 2023
PubMed
概括

这项研究提高了Mel频率塞普斯特拉系数 (MFCC) 的可解释性,用于使用语音生物标志物检测疾病. 研究人员发现,调整MFCC2计算参数可以提高其对疾病引起的语音变化的敏感性.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

761
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

相关实验视频

Last Updated: Jul 7, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

761
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

科学领域:

  • 语音处理 语音处理
  • 生物医学工程 生物医学工程
  • 临床诊断 临床诊断 临床诊断

背景情况:

  • 语音生物标志物显示出疾病检测的希望,但往往缺乏临床解释性.
  • 梅尔频率塞普斯特尔系数 (MFCC) 是已知的语音标记,但被认为是不可解释的.
  • 现有的方法努力弥合复杂的信号处理特征和临床理解之间的差距.

研究的目的:

  • 调查MFCC和可解释语音生物标志物之间的相关性.
  • 提高MFCC在疾病检测中的临床可解释性.
  • 提高MFCC对疾病引起的声音变化的敏感性.

主要方法:

  • 探索MFCC系数与已确定的语音生物标志物之间的相关性.
  • 量化MFCC2终点,将其与低频与高频的能量比率相关.
  • 在多个数据集中分析MFCC2性能,并调整计算参数.

主要成果:

  • 确定了MFCC系数和可解释的语音特征之间的显著相关性.
  • 证明MFCC2可以解释为低频与高频能量的比.
  • 通过优化计算参数,展示了MFCC2对疾病的敏感性.

结论:

  • 通过将它们与已建立的语音生物标志物联系起来,可以使MFCC更具临床解释性.
  • MFCC2终点提供了与疾病引起的语音变化相关的可量化的测量.
  • 优化MFCC计算参数可以显著提高用于疾病检测的语音生物标志物的实用性.