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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.
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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相关实验视频

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一个基于深度学习的可调整强制调整系统:对儿童言语的应用.

Prad Kadambi1,2, Tristan J Mahr3, Katherine C Hustad4

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概括

一个新的工具,Wav2TextGrid,可以为儿童的言语进行可训练的语音对齐,比基线方法提高了准确性. 这个系统直接训练手动对齐,增强临床级语音分析.

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科学领域:

  • 语音处理 语音处理
  • 计算语言学计算语言学
  • 语音学 语音学 语音学

背景情况:

  • 语音强制对齐对于自动语音分析至关重要,特别是对于儿童语音等非标准语音.
  • 手动对齐是黄金标准,但耗时,目前的工具不支持对这些对齐进行直接训练.

研究的目的:

  • 开发一个可训练的,适应扬声器的语音强制对齐系统,用于儿童的言语.
  • 为了提高准确度,可以直接培训手动对齐.

主要方法:

  • 开发了Wav2TextGrid,一种神经强制对齐器,使用了42名神经类型儿童 (3-6岁) 的身体.
  • 评估儿童语言表现和TIMIT语料库,以评估跨年龄和方言的适应能力.

主要成果:

  • 与基线相比,Wav2TextGrid工具显著提高了所有语音类别的对齐精度.
  • 在儿童的言语中实现了超过40%的精度改善,用于儿童的语音和语音.
  • 将现有方法与13分钟的标记数据相匹配;在45-60分钟内看到显著的改进.

结论:

  • Wav2TextGrid提供了一个替代的对齐工作流程,将强制对齐定制为与临床级手动对齐相匹配.
  • 该工具增强了对儿童语音和其他非标准语音模式的分析.