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成人依恋风格对情绪语音声学特征的作用

胡涵 顾文涛

胡涵, 顾文涛. 成人依恋风格对情绪语音声学特征的作用[J]. 声学学报. doi: 10.12395/0371-0025.2023051
引用本文: 胡涵, 顾文涛. 成人依恋风格对情绪语音声学特征的作用[J]. 声学学报. doi: 10.12395/0371-0025.2023051
HU Han, GU Wentao. The effects of adult attachment style on acoustic characteristics of emotional speech[J]. ACTA ACUSTICA. doi: 10.12395/0371-0025.2023051
Citation: HU Han, GU Wentao. The effects of adult attachment style on acoustic characteristics of emotional speech[J]. ACTA ACUSTICA. doi: 10.12395/0371-0025.2023051

成人依恋风格对情绪语音声学特征的作用

doi: 10.12395/0371-0025.2023051
基金项目: 国家社会科学基金项目(13&ZD189)及国家留学基金资助
详细信息
    通讯作者:

    顾文涛, wtgu@njnu.edu.cn

  • PACS: 43.66, 43.70

The effects of adult attachment style on acoustic characteristics of emotional speech

  • 摘要:

    为探究说话人的依恋类型(安全型、超脱型、专注型、恐惧型)对情绪语音产出的影响, 设计了符合语法规则但是无意义的伪句, 招募了有恋爱经验的被试, 采用阈下词汇启动范式激活依恋系统后, 被试观看4种基本情绪(开心、愤怒、悲伤、恐惧)的诱发影片, 用体验到的情绪向想象中的恋爱伴侣说出这些句子。对递归特征消除算法筛选出的每句14个声学参数做半参数重复测量多元方差, 结果显示依恋类型和情绪类别的主效应显著、交互效应不显著; 聚集性分层聚类分析发现, 在声学特征空间中, 超脱型和专注型距离最近, 而安全型则远离其他类型; 有监督分类发现, 14个声学参数可有效区分4类依恋类型; 特征重要性分析发现, 韵律参数对分类的贡献较大; 累积局部轮廓分析发现, 4类人群间基频特征的差异在各种情绪上基本一致, 但是音质特征的差异受到情绪类别的影响。研究揭示了依恋类型对情绪语音声学特征的作用, 验证了不同依恋类型在情绪调节策略上的差异, 为个性化人机语音交互技术的发展提供了科学依据。

     

  • 图 1  基于 4 类人群声学距离的系统树

    图 2  特征重要性分析结果

    注:条形图表示特征重要性分数的均值;箱线图表示特征重要性分数的分布;垂直虚线表示置换给定特征前后交叉熵不变时的重要性分数。

    图 3  ALP 分析结果

    注:灰度表示对ALP曲线做OPR拟合的线性项系数的绝对值;—代表系数为负值;☆代表语音特征较大(> M+SD)时对应的决策概率最高的依恋类型;〇代表语音特征较小(< M–SD)时对应的决策概率最高的依恋类型;网格表示线性项系数不显著。

    表  1  不同依恋类型被试的基本信息比较

    背景变量总计
    N = 48
    安全型
    N = 18
    超脱型
    N = 7
    专注型
    N = 7
    恐惧型
    N = 16
    四类比较的p
    离散变量的分布性别     0.391
    35 (73%)15 (83%)6 (86%)4 (57%)10 (62%) 
    13 (27%)3 (17%)1 (14%)3 (43%)6 (38%) 
    恋爱次数     0.505
    118 (38%)7 (39%)3 (43%)4 (57%)4 (25%) 
    218 (38%)8 (44%)1 (14%)2 (29%)7 (44%) 
    38 (17%)3 (17%)1 (14%)1 (14%)3 (19%) 
    44 (8%)0 (0%)2 (29%)0 (0%)2 (12%) 
    是否正在恋爱     < 0.001
    18 (38%)1 (6%)3 (43%)3 (43%)11 (69%) 
    30 (62%)17 (94%)4 (57%)4 (57%)5 (31%) 
    是否异地恋     0.489
    12 (40%)7 (41%)3 (75%)1 (25%)1 (20%) 
    18 (60%)10 (59%)1 (25%)3 (75%)4 (80%) 
    连续变量的
    均值(标准差)
    年龄24.10 (3.08)23.78 (2.02)24.43 (3.51)23.71 (0.95)24.50 (4.40)0.878
    年龄绝对差值6.67 (6.39)7.29 (6.79)4.50 (4.43)6.75 (5.62)6.20 (8.17)0.748
    恋爱阶段2.90 (0.80)2.76 (0.83)3.25 (0.96)3.50 (0.58)2.60 (0.55)0.248
    下载: 导出CSV

    表  2  机器学习算法的分类预测结果

     LDASVMRFXGBTMPM
    所有情绪ACC (95%CI)0.48 (0.45, 0.52)0.81 (0.78, 0.83)0.83 (0.80, 0.85)0.82 (0.79, 0.84)0.82 (0.79, 0.85)
    F10.52; 0.40; 0.49; 0.490.87; 0.76; 0.73; 0.800.88; 0.80; 0.70; 0.840.87; 0.79; 0.73; 0.810.86; 0.78; 0.74; 0.83
    开心ACC (95%CI)0.47 (0.40, 0.54)0.72 (0.65, 0.78)0.78 (0.72, 0.84)0.80 (0.74, 0.85)0.69 (0.62, 0.75)
    F10.54; 0.32; 0.54; 0.430.82; 0.69; 0.65; 0.630.84; 0.76; 0.71; 0.760.84; 0.79; 0.72; 0.780.76; 0.62; 0.68; 0.62
    愤怒ACC (95%CI)0.41 (0.34, 0.48)0.66 (0.59, 0.72)0.74 (0.68, 0.80)0.73 (0.67, 0.79)0.66 (0.59, 0.72)
    F10.51, 0.30; 0.45; 0.350.75; 0.52; 0.48; 0.690.83; 0.72; 0.60; 0.730.82; 0.71; 0.58; 0.720.76; 0.55; 0.57; 0.64
    悲伤ACC (95%CI)0.51 (0.44, 0.58)0.78 (0.72, 0.84)0.74 (0.67, 0.79)0.74 (0.67, 0.79)0.75 (0.68, 0.80)
    F10.55; 0.49; 0.49; 0.510.78; 0.78; 0.63; 0.860.74; 0.80; 0.68; 0.720.74; 0.86; 0.64; 0.720.76; 0.78; 0.60; 0.78
    恐惧ACC (95%CI)0.48 (0.41, 0.55)0.74 (0.67, 0.79)0.76 (0.70, 0.82)0.72 (0.66, 0.78)0.67 (0.61, 0.74)
    F10.55; 0.39; 0.40; 0.500.78; 0.72; 0.67; 0.720.83; 0.64; 0.64; 0.780.78; 0.62; 0.60; 0.750.74; 0.57; 0.61; 0.68
    注: ACC为准确率, 4个F1值分别对应安全型、超脱型、专注型和恐惧型。灰色单元指预测效果最佳的模型。
    下载: 导出CSV

    A1  纳入正式实验的15个目标伪句

    目标句自然度均值自然度标准差句长
    (音节数)
    你们泡到羊表里3.601.597
    他们踩到道杯中3.731.337
    他们往车桌里排3.671.237
    我们到尾边打澡4.131.067
    你角里铺着个豆仔4.071.338
    你们在光海上跳球4.400.838
    你在排房上晒春某4.530.648
    他的发签上沾了麻4.271.108
    他的胎上有个表票3.801.478
    他在坊门里唱了一个绳4.201.0110
    她从包盒里拿出一个诗4.600.6310
    你们到线桥上切了一个棋3.931.2811
    他们从角客里偷了一个盘4.730.5911
    她们到枕尺里救了一个盐4.001.5111
    我们去心沙里丢了一个发3.731.6211
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-09
  • 修回日期:  2023-06-25
  • 网络出版日期:  2023-09-27

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