Emotion Recognition With Multimodal Transformer Fusion Framework Based on Acoustic and Lexical Information

Authors:
Publisher website
Google Scholar
Abstract
People usually express emotions through paralinguistic and linguistic information in speech. How to effectively integrate linguistic and paralinguistic information for emotion recognition is a challenge. Previous studies have adopted the bidirectional long short-term memory (BLSTM) network to extract acoustic and lexical representations followed by a concatenate layer, and this has become a common method. However, the interaction and influence between different modalities are difficult to promote using simple feature fusion for each sentence. In this article, we propose an implicitly aligned multimodal transformer fusion (IA-MMTF) framework based on acoustic features and text information. This model enables the two modalities to guide and complement each other when learning emotional representations. Thereafter, the weighed fusion is used to control the contributions of different modalities. Thus, we can obtain more complementary emotional representations. Experiments on the interactive emotional dyadic motion capture (IEMOCAP) database and multimodal emotionlines dataset (MELD) show that the proposed method outperforms the baseline BLSTM-based method.
Funding Information
  • National Key R&D Program of China (2018YFB1305200)
  • National Natural Science Foundation of China (62176182, 61976216)
  • Tianjin Municipal Science and Technology (19ZXZNGX00030)

This publication has 16 references indexed in Scilit: