Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM

Bagus Tris Atmaja*, Masato Akagi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


Automatic speech emotion recognition (SER) by a computer is a critical component for more natural human-machine interaction. As in human-human interaction, the capability to perceive emotion correctly is essential to taking further steps in a particular situation. One issue in SER is whether it is necessary to combine acoustic features with other data such as facial expressions, text, and motion capture. This research proposes to combine acoustic and text information by applying a late-fusion approach consisting of two steps. First, acoustic and text features are trained separately in deep learning systems. Second, the prediction results from the deep learning systems are fed into a support vector machine (SVM) to predict the final regression score. Furthermore, the task in this research is dimensional emotion modeling, because it can enable deeper analysis of affective states. Experimental results show that this two-stage, late-fusion approach, obtains higher performance than that of any one-stage processing, with a linear correlation from one-stage to two-stage processing. This late-fusion approach improves previous early fusion result measured in concordance correlation coefficients score.

Original languageEnglish
Pages (from-to)9-21
Number of pages13
JournalSpeech Communication
Publication statusPublished - Feb 2021


  • Affective computing
  • Automatic speech emotion recognition
  • Bimodal fusion
  • Dimensional emotion
  • Late fusion


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