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首页> 外文会议>Humaine Association Conference on Affective Computing and Intelligent Interaction >Depression Detection amp; Emotion Classification via Data-Driven Glottal Waveforms
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Depression Detection amp; Emotion Classification via Data-Driven Glottal Waveforms

机译：抑郁症检测和情感分类通过数据驱动的光泽波形

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This doctoral consortium paper outlines the author's proposed investigation into the use of the voice-source waveform for affective computing. A data-driven glottal waveform representation, previously examined in the authors earlier doctoral studies for its speaker discriminative abilities, is proposed to be studied for both depression detection and emotion recognition, including severity classification when considering depression. 'Data-driven' refers to a parameterisation focus on the small but consistent idiosyncrasies of the glottal wave rather than only the mean shape and ratio measures. A review of the literature is given covering existing studies of the glottal waveform for depression detection and emotion classification. The benefits of developing easily accessible automatic recognition systems is stressed. The value of developing objective tools for clinicians in diagnosing depression is also conveyed. Finally research questions are framed and experimental methodologies discussed in order to address these. The studies proposed here will expand the body of knowledge regarding the information content of the glottal waveform and aim to improve depression detection and emotion classification accuracies based on the voice-source alone.
机译：该博士组织纸张概述了作者提出了对使用情感计算的语音源波形的调查。先前在提交人中审查的数据驱动的声门波形表示，提出了其扬声器歧视能力的博士研究，以研究抑郁症检测和情感认可，包括考虑抑郁时的严重程度分类。 “数据驱动”是指参数化专注于光学波浪的小但一致的特质，而不是仅仅是平均形状和比率测量。给出了文献的审查，涵盖了对抑郁症检测和情感分类的发光波形的现有研究。强调了开发易于访问的自动识别系统的好处。还传达了在诊断抑制诊断临床医生的目标工具的价值。最后研究问题是讨论的框架和实验方法，以解决这些问题。这里提出的研究将扩展关于声门波形信息内容的知识体系，并旨在根据单独的语音来源改善基于语音源的抑郁检测和情感分类精度。

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