普罗索迪对机器分类精神分裂症的贡献
Tomer Ben Moshe1, Ido Ziv2, Nachum Dershowitz1
1Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
语音中的声学特征,如音调,可以通过计算来检测精神分裂症的症状. 这些假象特征比单独的文本分析更有效地识别精神分裂症.
科学领域:
- 计算语言学计算语言学
- 精神病学是一个精神病学.
- 语音分析 语音分析
背景情况:
- 精神分裂症的检测通常依赖于语音的文字分析.
- 对于精神分裂症的检测,声学体特征尚未得到充分的探索.
研究的目的:
- 调查声学体特征在检测精神分裂症症状方面的有效性.
- 将声学特征的诊断能力与传统的基于文本的特征进行比较.
主要方法:
- 从口语反应中利用声学前奏特征 (音调,暂停) 的计算分析.
- 将声学特征的分类性能与基于文本的特征进行比较.
- 评估声学和文本特征对诊断准确性的综合影响.
主要成果:
- 与文本特征相比,声学假象特征在检测精神分裂症方面表现优越.
- 当与声学特征相结合时,文本特征只提供了边际的改进.
- 语音声学的计算分析为精神分裂症评估提供了一个有希望的途径.
结论:
- 声学体特征对于计算机检测精神分裂症症状非常有效.
- 基于语音的声学分析可以在识别精神分裂症时优于基于文本的方法.
- 整合声学特征可以提高精神分裂症检测算法的准确性.
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