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相关概念视频

Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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机器学习算法的评估性比较用于口检测和分类.

Ramitha V1, Rhea Chainani1, Saharsh Mehrotra1

  • 1Department of Artificial Intelligence and Machine Learning, Symbiosis International University Symbiosis Institute of Technology, Pune, Maharashtra, India.

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|December 9, 2024
PubMed
概括

这项研究引入了自动口吃检测,以帮助语音治疗,并改善口吃者 (PWS) 的语音识别. 机器学习模型与SEP-28k数据集进行了比较,以分类口吃类型.

关键词:
自动失流感检测自动失流感检测进行比较分析.机器学习 机器学习语言障碍 语音障碍 语音障碍 语音障碍自语 自语 自语 自语支持矢量分类器,随机森林,决策树,K-最近邻居,后勤回归.

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科学领域:

  • 语音语言病理学 语言病理学
  • 计算语言学 计算语言学
  • 机器学习 机器学习

背景情况:

  • 吃是一种神经发育发言障碍,影响社会和职业生活.
  • 自动检测口吃可以支持语言治疗师,并增强口吃 (PWS) 的人的语音识别.

研究的目的:

  • 进行机器学习模型的比较分析,用于自动检测口吃事件.
  • 将五种不同类型的流失分类为:延长,插入,单词重复,声音重复和块.
  • 评估声学特征对模型性能的影响,并解决类失衡的挑战.

主要方法:

  • 用SEP-28k数据集进行模型培训和评估.
  • 在各种机器学习模型中进行了比较分析.
  • 研究了声学特征对分类准确性的影响.

主要成果:

  • 不同机器学习模型用于口检测的比较性能评估.
  • 识别关键的声学特征,有助于准确的失流性分类.
  • 评估模型在处理数据集中的类不平衡时的稳定性.

结论:

  • 自动语检测系统可以有效地支持语言语言病理学家.
  • 机器学习模型在改善的人 (PWS) 的语音识别方面表现有前途.
  • 对声学特征和阶级不平衡的进一步研究对于推进口检测技术至关重要.