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相关概念视频

Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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基于机器学习的声严重程度的估计使用持续的元音)

Tobias Schraut1, Anne Schützenberger1, Tomás Arias-Vergara1

  • 1Division of Phoniatrics and Pediatric Audiology at the Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.

The Journal of the Acoustical Society of America
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概括

本研究引入了一种客观的机器学习方法,用于评估声的严重程度,使用持续元音发音的声学特征,为主观语音质量评估提供更可靠的替代方案.

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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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科学领域:

  • 语音科学是一种语言科学.
  • 声学分析 声学分析
  • 机器学习在医疗保健中的应用

背景情况:

  • 听觉感知评估是语音质量的标准,但受到主观性和有限尺度的影响.
  • 需要采取客观措施来克服评价者之间的变化,并改进声评估.

研究的目的:

  • 开发一种持续的,客观的方法来评估声的严重程度.
  • 将机器学习与持续发音的声学分析相结合.
  • 为了将客观的测量与主观的声评级相对应.

主要方法:

  • 从595名受试者中收集了635个持续/a/元音的声学录音,并获得了相应的主观声评分.
  • 提取了50个时间,光谱和面特征,使用统计分析选择了一个子集.
  • 使用后勤回归 (LR) 来分类声水平并生成概率得分.

主要成果:

  • 通过使用五个声学特征和LR,在模型预测和主观评分之间实现了0.867准确度和0.805相关性.
  • 在模型预测和主观声变化治疗前后之间显示出高质量一致.
  • 在治疗前和治疗后的评估中获得了0.567的适度定量相关性.

结论:

  • 使用机器学习的拟议定量方法显示出对客观声严重程度估计的重大前景.
  • 这种方法有可能提高语音质量评估的可靠性和客观性.
  • 进一步的研究可以完善这种技术,用于语音障碍诊断和治疗监测的临床应用.