基于人工智能方法分析和纠正发音障碍
Nataliia Melnykova1, Bohdan Pavlyk1, Oleh Basystiuk1
1Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine.
Frontiers in artificial intelligence
|June 20, 2025
概括
人工智能和机器学习有效地评估创伤的军事患者的发音障碍. 一个卷积神经网络模型在分类脱节症阶段方面略高于长期短期记忆网络.
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
- 人工智能的人工智能
背景情况:
- 创伤,特别是军事人员的创伤,往往导致沟通障碍,如发音障碍.
- 有效的康复对受影响个人的生活质量和重新融入至关重要.
- 评估和纠正发音障碍的现有方法可能需要通过先进技术进行增强.
研究的目的:
- 使用人工智能 (AI) 和机器学习 (ML) 来评估和纠正创伤后军事患者的发音障碍.
- 为了比较不同ML模型在分类语音障碍方面的有效性.
- 开发一个基础,以改善沟通康复战略.
主要方法:
- 文献审查以确定合适的AI/ML技术.
- 选择和实施两种实验模型:卷积神经网络 (CNN) 具有 mel-spectrograms 和长短期记忆 (LSTM) 具有 mel-frequency cepstral 系数.
- 使用相关数据集对模型进行培训和验证,然后对性能指标进行比较分析.
主要成果:
- 两种CNN和LSTM模型都表现出有前途的功能,用于分类失节症的阶段.
- 与LSTM模型相比,CNN模型在预测所有分类类别方面表现略高.
- 使用损失函数,准确性,错误矩阵,精度,回忆和F1分数来评估性能.
结论:
- 人工智能和机器学习方法显示出对在后创伤性军事群体中客观评估发音障碍的巨大潜力.
- 使用Mel光谱的CNN方法在这个任务中显得特别有效.
- 进一步的研究可以完善这些模型,用于语音康复的临床应用.
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