Autoencoder With Emotion Embedding for Speech Emotion Recognition
نویسندگان
چکیده
An important part of the human-computer interaction process is speech emotion recognition (SER), which has been receiving more attention in recent years. However, although a wide diversity methods proposed SER, these approaches still cannot improve performance. A key issue low performance SER system how to effectively extract emotion-oriented features. In this paper, we propose novel algorithm, an autoencoder with embedding, deep Unlike many previous works, instance normalization, common technique style transfer field, introduced into our model rather than batch normalization. Furthermore, embedding path method can lead efficiently learn priori knowledge from label. It enable distinguish features are most related human emotion. We concatenate latent representation learned by and acoustic obtained openSMILE toolkit. Finally, concatenated feature vector utilized for classification. To generalization method, simple data augmentation approach applied. Two publicly available highly popular databases, IEMOCAP EMODB, chosen evaluate method. Experimental results demonstrate that achieves significant improvement compared other systems.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3069818