使用语音数据对喉疾病进行分类:八频段与中频段波器对比
Jaemin Song1, Hyunbum Kim2, Yong Oh Lee1
1Department of Industrial and Data Engineering, Hongik University, Seoul, South Korea.
Heliyon
|December 25, 2024
概括
使用1/3八度波段过器的八度频谱能量 (OFSE) 在诊断喉疾病方面显示出比Mel频率脑膜系数 (MFCC) 更高的准确性. 这种人工智能驱动的语音分析为早期喉癌检测提供了一种有希望的非侵入性方法.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 喉癌的诊断传统上依赖于专家检查.
- 使用语音分析的非侵入性诊断方法正在随着人工智能的进步而出现.
- 梅尔频率脑系数 (MFCC) 是语音分析的标准,但对于微妙的喉变化可能缺乏准确性.
研究的目的:
- 为了比较MFCC和八度频谱能量 (OFSE) 在分类语音数据中的诊断效率.
- 使用人工智能区分健康的声音,喉癌,良性粘膜疾病和声麻.
- 评估OFSE使用1/3八度波段过器来改善喉疾病的检测.
主要方法:
- 使用卷积神经网络 (CNN) 模型分析了363名患者的语音样本.
- 使用MFCC和OFSE的1/3八度波段过器进行特征提取.
- 使用Grad-Class激活映射 (Grad-CAM) 来可视化关键的语音特征.
主要成果:
- 与MFCC (0.7061 ± 0.0561) 相比,使用1/3八度波段过器的 OFSE 实现了显著更高的分类准确性 (0.9398 ± 0.0232).
- 格拉德-CAM确定了喉癌的独特语音特征,包括过度形成区域的噪音和基本的频率变化.
- 人工智能在良性疾病和喉癌之间进行区分仍然具有挑战性,因为声音特征重叠.
结论:
- 使用1/3八度波段过器的OFSE在诊断喉疾病,包括癌症方面表现出卓越的性能.
- 这种基于人工智能的语音分析方法显示了准确,非侵入性早期检测喉疾病的潜力.
- 需要进一步的研究,以解决使用语音数据区分良性粘膜病与喉癌的挑战.
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