基于1DCNN-BiLSTM的在线医疗服务的音调分类
Cheng Huang1, Peng Xie2, Chunming Wu1
1College of Computer and Information Science, Southwest University, Beibei District, Chongqing, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一种结合一维卷积神经网络 (1DCNN) 和双向长期短期记忆 (BiLSTM) 网络的新型模型,以改进在线医疗服务中的医生音调分类. 该模型实现了84.4%的识别率,增强了医生与患者之间的沟通.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 医疗信息学 医疗信息学
背景情况:
- 在线医疗服务中,准确的音调分类对于有效的医生与患者沟通至关重要.
- 在这种情况下,现有的方法可能无法完全捕捉医生演讲的复杂声学特征.
研究的目的:
- 开发和评估一种新型模型,以提高在线医疗场景中医生音调分类的识别率.
- 通过更好的语音分析,提高医生与患者沟通的效率和质量.
主要方法:
- 提出了一个混合模型,集成一个一维的卷积神经网络 (1DCNN) 进行本地特征提取和一个双向的长期短期记忆 (BiLSTM) 网络进行全球序列特征捕获.
- 一项调查确定了重要的音调类型,并使用Librosa提取了68个时间和频域特征.
- 功能级融合结合了1DCNN和BiLSTM的优势.
主要成果:
- 拟议的1DCNN-BiLSTM模型在在线医疗服务场景中实现了84.4%的平均识别率和84.4%的F1得分.
- 该模型在音色分类准确性方面明显优于现有方法.
- 废弃实验证实了单个模块 (1DCNN,BiLSTM) 和参数设置的有效性.
结论:
- 集成的1DCNN-BiLSTM模型在在线医疗环境中显著提高了医生语音分类准确性.
- 这种方法有效地提高了医生和患者之间的沟通,通过提供更好的洞察力声音线索.
- 该研究验证了混合深度学习架构在专业领域的复杂音频分析中的实用性.
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