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Shape-based modeling of the fundamental frequency contour for emotion detection in speech

机译：基于形状的语音情感检测基频轮廓建模

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摘要

This paper proposes the use of neutral reference models to detect local emotional prominence in the fundamental frequency. A novel approach based on functional data analysis (FDA) is presented, which aims to capture the intrinsic variability of FO contours. The neutral mgdels are represented by a basis of functions and the testing FO contour is characterized by the projections onto that basis. For a given FO contour, we estimate the functional principal component analysis (PCA) projections, which are used as features for emotion detection. The approach is evaluated with lexicon-dependent (i.e., one functional PCA basis per sentence) and lexicon-independent (i.e., a single functional PCA basis across sentences) models. The experimental results show that the proposed system can lead to accuracies as high as 75.8% in binary emotion classification, which is 6.2% higher than the accuracy achieved by a benchmark system trained with global FO statistics. The approach can be implemented at sub-sentence level (e.g., 0.5 s segments), facilitating the detection of localized emotional information conveyed within the sentence. The approach is validated with the SEMAINE database, which is a spontaneous corpus. The results indicate that the proposed scheme can be effectively employed in real applications to detect emotional speech.
机译：本文提出使用中性参考模型来检测基本频率中的局部情绪突出。提出了一种基于功能数据分析（FDA）的新颖方法，旨在捕获FO轮廓的内在变化。中性mgdel由功能的基础表示，测试FO轮廓由在该基础上的投影来表征。对于给定的FO轮廓，我们估计功能主成分分析（PCA）投影，这些投影用作情感检测的特征。使用依赖于词典的模型（即，每个句子一个功能性PCA基础）和不依赖词典的模型（即，基于句子的单个功能性PCA基础）模型对该方法进行评估。实验结果表明，所提出的系统在二元情感分类中可导致高达75.8％的准确度，比通过使用全球FO统计数据训练的基准系统所达到的准确性高6.2％。该方法可以在子句级（例如，0.5s段）上实现，从而有助于检测在句子内传达的局部情感信息。该方法已通过SEMAINE数据库验证，该数据库是自发的语料库。结果表明，所提出的方案可以有效地应用于实际应用中，以检测情感语音。

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