An Interaction-aware Attention Network for Speech Emotion Recognition in Spoken Dialogs

Obtaining robust speech emotion recognition (SER) in scenarios of spoken interactions is critical to the developments of next generation human-machine interface. Previous research has largely focused on performing SER by modeling each utterance of the dialog in isolation without considering the tran...

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Bibliographic Details
Main Authors: Yeh, Sung-Lin, Lin, Yun-Shao, Lee, Chi-Chun
Format: Conference Proceeding
Language:English
Subjects:
Acoustics
attention mechanism
Benchmark testing
Context modeling
Emotion recognition
Feature extraction
interaction
Logic gates
speech emotion recognition
Speech recognition
spoken dialogs
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Summary:Obtaining robust speech emotion recognition (SER) in scenarios of spoken interactions is critical to the developments of next generation human-machine interface. Previous research has largely focused on performing SER by modeling each utterance of the dialog in isolation without considering the transactional and dependent nature of the human-human conversation. In this work, we propose an interaction-aware attention network (IAAN) that incorporate contextual information in the learned vocal representation through a novel attention mechanism. Our proposed method achieves 66.3% accuracy (7.9% over baseline methods) in four class emotion recognition and is also the current state-of-art recognition rates obtained on the benchmark database.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683293