成人依恋风格对情绪语音声学特征的作用

胡涵; 顾文涛

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

胡涵¹,
顾文涛^{1, 2, ,}

1.
南京师范大学文学院　南京　210097
2.
麦吉尔大学沟通科学与障碍学院　加拿大蒙特利尔　H3A1G1

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
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- 文章访问数: 93
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- 被引次数: 0
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

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

HU Han¹,
GU Wentao^{1, 2
, ,}

1.
School of Chinese Language and Literature, Nanjing Normal University　Nanjing　210097
2.
School of Communication Sciences and Disorders, McGill University　Montreal, Canada H3A1G1

摘要

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。
- 成人依恋类型 /
- 情绪语音 /
- 声学特征 /
- 情绪调节策略 /
- 有监督分类
Abstract:
This study explored the effect of a speaker’s attachment style (secure, detached, preoccupied, and fearful) on acoustic characteristics of emotional speech. Grammatical but meaningless pseudo sentences were designed, and participants with romantic relationship experiences were recruited. After activating their attachment systems with the subliminal lexical priming paradigm, the participants watched the videos that tended to evoke one of the four basic emotions (happiness, anger, sadness, and fear). Following this, they expressed these sentences with the corresponding emotions to their imagined romantic partners. Based on 14 acoustic parameters per utterance selected using the recursive feature elimination algorithm, Semi-Parametric Repeated Measures Multivariate Analysis of Variance shows significant main effects of attachment style and emotion type, but does not show a significant interaction effect between them. Agglomerative Hierarchical Cluster Analysis shows that, in the acoustic space, “dismissing” and “preoccupied” are the closest, while “secure” is the farthest from other attachment styles. Supervised classification algorithms effectively differentiate the four attachment styles based on 14 acoustic parameters, with prosodic parameters contributing more in terms of feature importance analysis. Furthermore, Accumulated-Local Profiles analysis indicates that the variations in fundamental frequency characteristics among the four attachment styles remain basically consistent across the four emotions, but the differences in timbre and voice quality characteristics are influenced by emotion type. In summary, this study unveils the impact of attachment style on emotional speech and confirms the variations in emotion regulation strategy among individuals with four attachment styles. This provides a scientific foundation for the development of personalized human-machine speech communication technologies.
- Adult attachment style /
- Emotional speech /
- Acoustic characteristics /
- Emotion regulation strategy /
- Supervised classification

HTML全文

图 1 基于 4 类人群声学距离的系统树

下载: 全尺寸图片幻灯片

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。

下载: 全尺寸图片幻灯片

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。

下载: 全尺寸图片幻灯片

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值
离散变量的分布	性别						0.391
	女	35 (73%)	15 (83%)	6 (86%)	4 (57%)	10 (62%)
	男	13 (27%)	3 (17%)	1 (14%)	3 (43%)	6 (38%)
	恋爱次数						0.505
	1	18 (38%)	7 (39%)	3 (43%)	4 (57%)	4 (25%)
	2	18 (38%)	8 (44%)	1 (14%)	2 (29%)	7 (44%)
	3	8 (17%)	3 (17%)	1 (14%)	1 (14%)	3 (19%)
	4	4 (8%)	0 (0%)	2 (29%)	0 (0%)	2 (12%)
	是否正在恋爱						< 0.001
	否	18 (38%)	1 (6%)	3 (43%)	3 (43%)	11 (69%)
	是	30 (62%)	17 (94%)	4 (57%)	4 (57%)	5 (31%)
	是否异地恋						0.489
	否	12 (40%)	7 (41%)	3 (75%)	1 (25%)	1 (20%)
	是	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

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表 2 机器学习算法的分类预测结果

		LDA	SVM	RF	XGBT	MPM
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)
所有情绪	F1	0.52; 0.40; 0.49; 0.49	0.87; 0.76; 0.73; 0.80	0.88; 0.80; 0.70; 0.84	0.87; 0.79; 0.73; 0.81	0.86; 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)
开心	F1	0.54; 0.32; 0.54; 0.43	0.82; 0.69; 0.65; 0.63	0.84; 0.76; 0.71; 0.76	0.84; 0.79; 0.72; 0.78	0.76; 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)
愤怒	F1	0.51, 0.30; 0.45; 0.35	0.75; 0.52; 0.48; 0.69	0.83; 0.72; 0.60; 0.73	0.82; 0.71; 0.58; 0.72	0.76; 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)
悲伤	F1	0.55; 0.49; 0.49; 0.51	0.78; 0.78; 0.63; 0.86	0.74; 0.80; 0.68; 0.72	0.74; 0.86; 0.64; 0.72	0.76; 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)
恐惧	F1	0.55; 0.39; 0.40; 0.50	0.78; 0.72; 0.67; 0.72	0.83; 0.64; 0.64; 0.78	0.78; 0.62; 0.60; 0.75	0.74; 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。

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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	3.60	1.59	7
他们踩到道杯中	3.73	1.33	7
他们往车桌里排	3.67	1.23	7
我们到尾边打澡	4.13	1.06	7
你角里铺着个豆仔	4.07	1.33	8
你们在光海上跳球	4.40	0.83	8
你在排房上晒春某	4.53	0.64	8
他的发签上沾了麻	4.27	1.10	8
他的胎上有个表票	3.80	1.47	8
他在坊门里唱了一个绳	4.20	1.01	10
她从包盒里拿出一个诗	4.60	0.63	10
你们到线桥上切了一个棋	3.93	1.28	11
他们从角客里偷了一个盘	4.73	0.59	11
她们到枕尺里救了一个盐	4.00	1.51	11
我们去心沙里丢了一个发	3.73	1.62	11

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