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Adversarially-enriched Acoustic Code Vector Learned from Out-of-context Affective Corpus for Robust Emotion Recognition
Abstract
Advancement in speech emotion recognition technology has brought tremendous potential in designing human-centered applications across a wide range of scenarios. However, due to the difficulty in obtaining large-scale labeled emotion corpus for every application domains, most of the existing databases are collected within disparate and limited contexts. This contextualization often undermines the variability in the emotional acoustic manifestation due to the limitation in the amount of labeled data that can be collected for each particular context. This, hence, creates a robustness issue across emotional scenarios. In this work, we propose to learn an enhanced acoustic code vector for in-context emotion database through adversarially learning from large out-of-context emotion corpus to obtain robust emotion recognition. We demonstrate that our framework can obtain improved recognition accuracy using low dimensional representations on two different databases, and it maintains its modeling power even when given very limited in-context training samples.
Figures
An emotion-enriched acoustic vector for in-context emotion data is learned adversarially by leveraging out-of-context emotion corpora, then SVM is used to perform final classification.
An emotion-enriched acoustic vector for in-context emotion data is learned adversarially by leveraging out-of-context emotion corpora, then SVM is used to perform final classification.
Keywords
behavioral signal processing (BSP) | adversarial network | emotion recognition | cross corpus learning
Authors
Chun-Min Chang Chi-Chun Lee
Publication Date
2019/05/12
Conference
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI
10.1109/icassp.2019.8683059
Publisher
IEEE