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相关概念视频

Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
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People can go to great lengths to protect their self-image and present themselves in ways that they want others to see them. Sociologist Erving Goffman presented the idea that a person is like an actor on a stage. Calling his theory dramaturgy, Goffman believed that we use “impression management” to present ourselves to others as we hope to be perceived. Each situation is a new scene, and individuals perform different roles depending on who is present (Goffman, 1959). Think about...
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通过语音检测体化障碍:引入自主监督学习模型

Zhihao Bao, Kun Qian, Zhonghao Zhao

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    概括
    此摘要是机器生成的。

    自主监督学习模型,特别是wav2vec 2.0,在从语音数据中识别体化障碍方面表现有前途,即使标签有限. 这种方法有助于精神病医生在临床诊断中,减少了对广泛数据标签的需求.

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    科学领域:

    • 精神病学和计算语言学
    • 人工智能在心理健康诊断中的应用

    背景情况:

    • 由于其微妙的表现,体化障碍的诊断不足.
    • 准确的语音识别用于体化障碍的识别需要大量的标记数据集,这些数据很少.
    • 现有的语音识别方法严重依赖于监督学习和大量标记数据.

    研究的目的:

    • 调查自主监督学习 (SSL) 模型的有效性,以识别有限的标记数据的言语体化障碍.
    • 为了比较三个SSL模型的性能:对比预测编码 (CPC),wav2vec和wav2vec 2.0.0.
    • 建立一种更有效的方法来开发用于体化障碍的诊断工具.

    主要方法:

    • 使用了三种预先训练的自我监督学习模型:CPC,wav2vec和wav2vec 2.0.
    • 将这些模型应用于几个标记的体化障碍语音数据集.
    • 将SSL模型的性能与监督学习基准进行了比较.

    主要成果:

    • wav2vec 2.0 模型以 77.0% 的未加权平均回忆率获得了最高的性能.
    • wav2vec 2.0 模型的表现明显超过了 CPC 模型 (p < .005).
    • 最好的SSL模型超过了传统的监督学习模型的性能.

    结论:

    • 自主监督学习,特别是 wav2vec 2.0,对于具有有限标记数据的体化障碍语音识别是有效的.
    • 这种方法可以显著减少临床应用手动数据标签的负担.
    • 开发的模型提供了一个有价值的工具,以协助精神病医生在临床诊断的体质化障碍.