Analysis of couple interactions using speech processing techniques is an increasingly active multi-disciplinary field that poses challenges such as automatic relationship quality assessment and behavioral coding. Here, we focused on the prediction of individuals’ attachment style using interactions of recently married (1–15 months) couples. For low-level acoustic feature extraction, in addition to the frame-based acoustic features such as mel-frequency cepstral coefficients (MFCCs) and pitch, we used the turn-based i-vector features that are the commonly used in speaker verification systems. Sentiments, positive and negative, of the dialog turns were also automatically generated from transcribed text and used as features. Feature and score fusion algorithms were used for low-level acoustic features and text features. Even though score and feature fusion algorithms performed similar, predictions with score fusion were more consistent when couples have known each other for a longer period of time.

中文翻译：

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通过对话分析自动检测已婚夫妇的依恋类型

使用语音处理技术分析夫妻互动是一个日益活跃的多学科领域，它提出了自动关系质量评估和行为编码等挑战。在这里，我们专注于使用最近结婚（1-15 个月）夫妇的互动来预测个人的依恋风格。对于低级声学特征提取，除了基于帧的声学特征（例如梅尔频率倒谱系数 (MFCC) 和音高）之外，我们还使用了说话人验证系统中常用的基于回合的 i-vector 特征。对话轮次的正面和负面情绪也从转录的文本中自动生成并用作特征。特征和分数融合算法用于低级声学特征和文本特征。