DAVE: Detecting Agitated Vocal Events.

Asif Salekin, Hongning Wang, Kristine Williams, John Stankovic
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Abstract

DAVE is a comprehensive set of event detection techniques to monitor and detect 5 important verbal agitations: asking for help, verbal sexual advances, questions, cursing, and talking with repetitive sentences. The novelty of DAVE includes combining acoustic signal processing with three different text mining paradigms to detect verbal events (asking for help, verbal sexual advances, and questions) which need both lexical content and acoustic variations to produce accurate results. To detect cursing and talking with repetitive sentences we extend word sense disambiguation and sequential pattern mining algorithms. The solutions have applicability to monitoring dementia patients, for online video sharing applications, human computer interaction (HCI) systems, home safety, and other health care applications. A comprehensive performance evaluation across multiple domains includes audio clips collected from 34 real dementia patients, audio data from controlled environments, movies and Youtube clips, online data repositories, and healthy residents in real homes. The results show significant improvement over baselines and high accuracy for all 5 vocal events.

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迪夫:侦测激动的声音事件。
DAVE是一套全面的事件检测技术,用于监控和检测5种重要的言语激动:寻求帮助、言语性挑逗、问题、诅咒和重复句子的谈话。DAVE的新颖之处包括将声学信号处理与三种不同的文本挖掘范式相结合,以检测需要词汇内容和声学变化才能产生准确结果的口头事件(寻求帮助、口头性暗示和问题)。为了检测重复句子中的咒骂和说话,我们扩展了词义消歧和顺序模式挖掘算法。这些解决方案适用于监测痴呆症患者、在线视频共享应用、人机交互(HCI)系统、家庭安全以及其他医疗保健应用。一项跨多个领域的综合性能评估包括从34名真实痴呆症患者中收集的音频片段、来自受控环境的音频数据、电影和Youtube片段、在线数据存储库以及真实家庭中的健康居民。结果显示,所有5个声音事件都比基线有了显著的改善和较高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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