Unsupervised learning in cross-corpus acoustic emotion recognition

Zixing Zhang · Felix Johannes Weninger · Martin Wöllmer · Björn Wolfgang Schuller

124 citations0/0 files storedIEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

One of the ever-present bottlenecks in Automatic Emotion Recognition is data sparseness. We therefore investigate the suitability of unsupervised learning in cross-corpus acoustic emotion recognition through a large-scale study with six commonly used databases, including acted and natural emotion speech, and covering a variety of application scenarios and acoustic conditions. We show that adding unlabeled emotional speech to agglomerated multi-corpus training sets can enhance recognition performance even in a challenging cross-corpus setting; furthermore, we show that the expected gain by adding unlabeled data on average is approximately half the one achieved by additional manually labeled data in leave-one-corpus-out validation.

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References

2011Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge 2006The eNTERFACE’05 Audio-Visual Emotion Database 2008The Vera am Mittag German audio-visual emotional speech database 2009OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit 2010Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies 2007The HUMAINE Database: Addressing the Collection and Annotation of Naturalistic and Induced Emotional Data 2009Being bored? Recognising natural interest by extensive audiovisual integration for real-life application 1997Design, recording and verification of a danish emotional speech database

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2020Semi-Supervised Speech Emotion Recognition With Ladder Networks 2018Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages 2015Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges 2019Curriculum Learning for Speech Emotion Recognition From Crowdsourced Labels 2019Analysis of Deep Learning Architectures for Cross-Corpus Speech Emotion Recognition

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