用预训练的深度学习模型对帕金森说话障碍评估的可行性研究,用于语音对文本分析
Kwang Hyeon Kim1, Byung-Jou Lee2, Hae-Won Koo2
1Clinical Research Support Center, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea.
Korean journal of neurotrauma
|October 7, 2024
概括
这项研究使用波向VEC模型对帕金森病 (PD) 患者的语音到文本分析进行了探索. 研究结果表明,PD语言障碍患者的诊断和沟通支持可能会得到改善.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 帕金森病 (PD) 经常导致语言障碍,影响沟通.
- 准确的语音到文本 (STT) 分析对于评估和管理这些语言障碍至关重要.
研究的目的:
- 评估预先训练的深度学习波-至-VEC模型的可行性,用于在患有PD相关语音障碍的个体中进行STT分析.
- 探索模型在分析语音特征和解码文本方面的有效性.
主要方法:
- 利用了来自健康对照 (HC) 和PD患者的公共语音录音数据集.
- 应用Wav2Vec模型进行语音到文本分析,包括单词字母分类和波形分析.
- 评估单词匹配概率和音节一致性,用于语音过程评估.
主要成果:
- Wav2Vec模型应用于PD患者数据,分析语音特征.
- 阅读句子的词识别精度为HC的0.31,PD的0.10.
- 在HC (299.10±16.79) 和PD (259.80±93.39) 组之间,平均词数有所不同.
结论:
- 这项研究证明了波对的潜力,可以增强在PD语言障碍中的STT分析.
- 结果表明开发临床工具的途径,用于PD的诊断,评估和沟通支持.
- 进一步的研究可以完善这些人工智能驱动的方法用于神经语音障碍管理.
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