35,247 research outputs found
Speech-based recognition of self-reported and observed emotion in a dimensional space
The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance
Employing Emotion Cues to Verify Speakers in Emotional Talking Environments
Usually, people talk neutrally in environments where there are no abnormal
talking conditions such as stress and emotion. Other emotional conditions that
might affect people talking tone like happiness, anger, and sadness. Such
emotions are directly affected by the patient health status. In neutral talking
environments, speakers can be easily verified, however, in emotional talking
environments, speakers cannot be easily verified as in neutral talking ones.
Consequently, speaker verification systems do not perform well in emotional
talking environments as they do in neutral talking environments. In this work,
a two-stage approach has been employed and evaluated to improve speaker
verification performance in emotional talking environments. This approach
employs speaker emotion cues (text-independent and emotion-dependent speaker
verification problem) based on both Hidden Markov Models (HMMs) and
Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. The approach is
comprised of two cascaded stages that combines and integrates emotion
recognizer and speaker recognizer into one recognizer. The architecture has
been tested on two different and separate emotional speech databases: our
collected database and Emotional Prosody Speech and Transcripts database. The
results of this work show that the proposed approach gives promising results
with a significant improvement over previous studies and other approaches such
as emotion-independent speaker verification approach and emotion-dependent
speaker verification approach based completely on HMMs.Comment: Journal of Intelligent Systems, Special Issue on Intelligent
Healthcare Systems, De Gruyter, 201
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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