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Emotion recognition in spontaneous and acted dialogues

机译：在自发和行为对话中的情感认可

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In this work, we compare emotion recognition on two types of speech: spontaneous and acted dialogues. Experiments were conducted on the AVEC2012 database of spontaneous dialogues and the IEMOCAP database of acted dialogues. We studied the performance of two types of acoustic features for emotion recognition: knowledge-inspired disfluency and nonverbal vocalisation (DIS-NV) features, and statistical Low-Level Descriptor (LLD) based features. Both Support Vector Machines (SVM) and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) were built using each feature set on each emotional database. Our work aims to identify aspects of the data that constrain the effectiveness of models and features. Our results show that the performance of different types of features and models is influenced by the type of dialogue and the amount of training data. Because DIS-NVs are less frequent in acted dialogues than in spontaneous dialogues, the DIS-NV features perform better than the LLD features when recognizing emotions in spontaneous dialogues, but not in acted dialogues. The LSTM-RNN model gives better performance than the SVM model when there is enough training data, but the complex structure of a LSTM-RNN model may limit its performance when there is less training data available, and may also risk over-fitting. Additionally, we find that long distance contexts may be more useful when performing emotion recognition at the word level than at the utterance level.
机译：在这项工作中，我们对两种类型的语音进行了比较情感认可：自发和行为对话。在AVEC2012的自发对话数据库和IEMocap数据库上进行了实验。我们研究了两种类型的音乐识别声学特征的性能：知识灵感的失风和非语言声毒（DIS-NV）特征，以及基于统计的低级描述符（LLD）的特征。支持向量机（SVM）和长短短期内存经常性神经网络（LSTM-RNN）是使用每个情绪数据库上设置的每个功能构建的。我们的工作旨在确定限制模型和功能效力的数据的方面。我们的研究结果表明，不同类型的功能和模型的性能受对话类型和培训数据量的影响。由于在行为对话中的频率低于自发对话，因此在识别自发对话中的情绪时，DIS-NV特征在识别中的情绪，但不能在被动对话时，DIS-NVS比LLD功能更低。当有足够的训练数据时，LSTM-RNN模型提供比SVM模型更好的性能，但LSTM-RNN模型的复杂结构可能会在可用培训数据较少时限制其性能，并且也可能会冒险过度拟合。另外，我们发现，当在单词水平处执行比在话语级别的情绪识别时，长距离上下文可能更有用。

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