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

Accuracy and Precision01:52

Accuracy and Precision

8.9K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.  Highly accurate...
8.9K
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

474
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
474
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

485
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
485

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

TRIM21 Alleviates Alcoholic Liver Fibrosis by Inhibiting Ferroptosis Through Regulation of IDO1 Ubiquitination.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

Case Report: Dual response to efgartigimod in myasthenia gravis and comorbid autoimmune disorders: a case series.

Frontiers in immunology·2026
Same author

Functional RNA splitting drove the evolutionary emergence of type V CRISPR-Cas systems from transposons.

Cell·2026
Same author

Exploring the metabolic mysteries: Mechanistic insights into lactylation-mediated regulation of autoimmune diseases.

Autoimmunity reviews·2026
Same author

In Situ Oil-Gas Separator Enabled Carrier-Free Photoacoustic Sensing of Acetylene.

Sensors (Basel, Switzerland)·2026
Same author

Novel biallelic LSS variants in autosomal recessive hypotrichosis simplex: insights from a multi-omics approach.

Human genetics·2025
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
查看所有相关文章

相关实验视频

Updated: Jul 17, 2025

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
12:43

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Published on: February 21, 2011

34.8K

探索实用指标,以支持自动语音识别评估.

E A Draffan1, Mike Wald1, Chaohai Ding1

  • 1ECS, University of Southampton, UK.

Studies in health technology and informatics
|August 28, 2023
PubMed
概括
此摘要是机器生成的。

单单单词错误率不足以评估自动语音识别的质量. 分析平行语言特征的新指标提高了学术环境中的转录准确性和包容性.

关键词:
自动语音识别自动语音识别标题标题 标题标题身体残疾就是残疾.纠正错误的纠正错误的纠正转录 转录 转录 转录 转录 转录单词错误率 字体错误率

更多相关视频

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
06:04

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

419
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

相关实验视频

Last Updated: Jul 17, 2025

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
12:43

A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS

Published on: February 21, 2011

34.8K
Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
06:04

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

419
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

科学领域:

  • 语音技术 语言技术
  • 人与计算机的交互
  • 机器学习 机器学习

背景情况:

  • 传统的自动语音识别 (ASR) 的文字错误率 (WER) 度量不能充分捕捉错误类型或原因,阻碍了开发人员的反.
  • 在学术环境中,转录和标题中的ASR错误会对理解产生负面影响,特别是对于残疾学生来说,由于特定领域的语言和非母语使用者.
  • 现有的ASR与诸如杂的环境和学术讲座中常见的专用术语等挑战作斗争.

研究的目的:

  • 讨论在评估ASR输出质量时,除了文字错误率之外,使用额外的指标.
  • 探索这些指标如何为ASR中的机器学习过程提供更好的反.
  • 通过改进ASR,促进虚拟会议系统中的更具包容性的实践.

主要方法:

  • 检查文本错误率在评估ASR准确性的局限性.
  • 在ASR评估中研究 paralinguistic特征 (时机,语调,语音质量,语音理解) 的结合.
  • 分析反机制,以增强ASR系统的机器学习过程.

主要成果:

  • 文字错误率为ASR错误的性质和原因提供了有限的洞察力.
  • 同语言特征为学术ASR提供了更丰富的语言细微差别理解,这对学术ASR至关重要.
  • 整合新的指标可以导致更准确和更具上下文意识的ASR输出.

结论:

  • 增强的ASR评估需要超越简单单词准确性的指标.
  • 结合并语分析可以提高ASR在复杂的学术环境中的表现.
  • 先进的ASR评估促进了虚拟通信平台更大的包容性和可访问性.