论文标题
培训语音情感分类器没有分类注释
Training speech emotion classifier without categorical annotations
论文作者
论文摘要
情绪表示，分类标签和连续空间中的维度描述有两个范式。因此，情绪识别任务可以视为分类或回归。这项研究的主要目的是研究这两种表示之间的关系，并提出仅使用尺寸注释的分类管道。所提出的方法包含一个回归模型，该模型经过训练，可以预测给定语音音频的尺寸表示中连续值的向量。该模型的输出可以使用映射算法解释为情感类别。我们研究了两个不同的语料库中三个功能提取器，三个神经网络架构和三种映射算法的组合的性能。我们的研究显示了通过回归方法的分类的优势和局限性。
There are two paradigms of emotion representation, categorical labeling and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. The proposed approach contains a regressor model which is trained to predict a vector of continuous values in dimensional representation for given speech audio. The output of this model can be interpreted as an emotional category using a mapping algorithm. We investigated the performances of a combination of three feature extractors, three neural network architectures, and three mapping algorithms on two different corpora. Our study shows the advantages and limitations of the classification via regression approach.
