2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPE-24.6
Paper Title DOMAIN-ADVERSARIAL AUTOENCODER WITH ATTENTION BASED FEATURE LEVEL FUSION FOR SPEECH EMOTION RECOGNITION
Authors Yuan Gao, Jiaxing Liu, Longbiao Wang, Tianjin University, China; Jianwu Dang, Japan Advanced Institute of Science and Technology, Japan
SessionSPE-24: Speech Emotion 2: Neural Networks for Speech Emotion Recognition
LocationGather.Town
Session Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Time:Wednesday, 09 June, 15:30 - 16:15
Presentation Poster
Topic Speech Processing: [SPE-ANLS] Speech Analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract Over the past two decades, although speech emotion recognition (SER) has garnered considerable attention, the problem of insufficient training data has been unresolved. A potential solution for this problem is to pre-train a model and transfer knowledge from large amounts of audio data. However, the data used for pre-training and testing originate from different domains, resulting in the latent representations to contain non-affective information. In this paper, we propose a domain-adversarial autoencoder to extract discriminative representations for SER. Through domain-adversarial learning, we can reduce the mismatch between domains while retaining discriminative information for emotion recognition. We also introduce multi-head attention to capture emotion information from different subspaces of input utterances. Experiments on IEMOCAP show that the proposed model outperforms the state-of-the-art systems by improving the unweighted accuracy by 4.15\%, thereby demonstrating the effectiveness of the proposed model.