40,812 research outputs found
Emotional State Categorization from Speech: Machine vs. Human
This paper presents our investigations on emotional state categorization from
speech signals with a psychologically inspired computational model against
human performance under the same experimental setup. Based on psychological
studies, we propose a multistage categorization strategy which allows
establishing an automatic categorization model flexibly for a given emotional
speech categorization task. We apply the strategy to the Serbian Emotional
Speech Corpus (GEES) and the Danish Emotional Speech Corpus (DES), where human
performance was reported in previous psychological studies. Our work is the
first attempt to apply machine learning to the GEES corpus where the human
recognition rates were only available prior to our study. Unlike the previous
work on the DES corpus, our work focuses on a comparison to human performance
under the same experimental settings. Our studies suggest that
psychology-inspired systems yield behaviours that, to a great extent, resemble
what humans perceived and their performance is close to that of humans under
the same experimental setup. Furthermore, our work also uncovers some
differences between machine and humans in terms of emotional state recognition
from speech.Comment: 14 pages, 15 figures, 12 table
Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema
In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011
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
The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism
The INTERSPEECH 2013 Computational Paralinguistics Challenge provides for the first time a unified test-bed for Social Signals such as laughter in speech. It further introduces conflict in group discussions as new tasks and picks up on autism and its manifestations in speech. Finally, emotion is revisited as task, albeit with a broader ranger of overall twelve emotional states. In this paper, we describe these four Sub-Challenges, Challenge conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants.
\em Bj\"orn Schuller, Stefan Steidl, Anton Batliner, Alessandro Vinciarelli, Klaus Scherer}\\
{\em Fabien Ringeval, Mohamed Chetouani, Felix Weninger, Florian Eyben, Erik Marchi, }\\
{\em Hugues Salamin, Anna Polychroniou, Fabio Valente, Samuel Kim
First impressions: A survey on vision-based apparent personality trait analysis
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft
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