"Of All Things the Measure Is Man" : Automatic Classification of Emotions and Inter-Labeler Consistency

Steidl, Stefan; Levit, Michael; Batliner, Anton; Nöth, Elmar; Niemann, Heinrich

doi:10.1109/icassp.2005.1415114

Cited by 66 publications

(75 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The labels were mapped onto two cover classes by clustering according to a threshold over the average of all voters' labels as described in (Burkhardt et al, 2009). Following Davies extension of Cohen's Kappa (Davies and Fleiss, 1982) for multiple labelers we obtain a value of = 0.52 which corresponds to moderate inter labeler agreement (Steidl et al, 2005). Finally, our training setup contains 1761 angry turns and 2502 non-angry turns.…”

Section: Selected Corporamentioning

confidence: 99%

Salient Features for Anger Recognition in German and English IVR Portals

Polzehl

Schmitt

Metze

2010

Spoken Dialogue Systems Technology and Design

View full text Add to dashboard Cite

Anger recognition in speech dialogue systems can help to enhance human computer interaction. In this paper we report on the setup and performance optimization techniques for successful anger classification using acoustic cues. We evaluate the performance of a broad variety of features on both a German and an American English voice portal database which contain "real" speech, i.e. non-acted, continuous speech of narrow-band quality. Starting with a large-scale feature extraction, we determine optimal sets of feature combinations for each language, by applying an Information-Gain based ranking scheme. Analyzing the ranking we notice that a large proportion of the most promising features for both databases are derived from MFCC and loudness. In contrast to this similarity also pitch features proved importance for the English database. We further calculate classification scores for our setups using discriminative training and Support-Vector Machine classification. The developed systems show that anger recognition in both English and Salient Features for Anger Recognition in German and English IVR PortalsGerman language can be processed very similarly reaching comparable results.

show abstract

Section: Selected Corporamentioning

confidence: 99%

Salient Features for Anger Recognition in German and English IVR Portals

Polzehl

Schmitt

Metze

2010

Spoken Dialogue Systems Technology and Design

View full text Add to dashboard Cite

show abstract

“…This more balanced 4-class problem, which we refer to as AMEN, consists of 1557 words for Angry (A), 1224 words for Motherese (M), 1645 words for Emphatic (E), and 1645 for Neutral (N), cf. Steidl et al (2005). Cases where less than three labellers agreed were omitted, as well as cases labelled with other than these four main classes.…”

mentioning

confidence: 99%

Whodunnit – Searching for the most important feature types signalling emotion-related user states in speech

Batliner

Steidl

Schuller

et al. 2011

Computer Speech & Language

128

View full text Add to dashboard Cite

In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states -confined to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at different sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of 'most important' features which we try to interpret by discussing the impact of different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics.

show abstract

“…To date, studies on emotion recognition have been based on facial expressions (Zeng, Pantic, Roisman, & Huang, 2009), or focused on physiological indicators (Calvo & D'Mello, 2010) or voice recognition (Steidl, Levit, Batliner, Nöth, & Niemann, 2005). For example, Lee and colleagues (Lee, Narayanan, & Pieraccini, 2001) compared several classification rates by measuring speech of man versus women or changing feature extraction methods.…”

mentioning

confidence: 99%

Development of an auditory emotion recognition function using psychoacoustic parameters based on the International Affective Digitized Sounds

et al. 2014

View full text Add to dashboard Cite

The purpose of this study was to develop an auditory emotion recognition function that could determine the emotion caused by sounds coming from the environment in our daily life. For this purpose, sound stimuli from the International Affective Digitized Sounds (IADS-2), a standardized database of sounds intended to evoke emotion, were selected, and four psychoacoustic parameters (i.e., loudness, sharpness, roughness, and fluctuation strength) were extracted from the sounds. Also, by using an emotion adjective scale, 140 college students were tested to measure three basic emotions (happiness, sadness, and negativity). From this discriminant analysis to predict basic emotions from the psychoacoustic parameters of sound, a discriminant function with overall discriminant accuracy of 88.9 % was produced from training data. In order to validate the discriminant function, the same four psychoacoustic parameters were extracted from 46 sound stimuli collected from another database and substituted into the discriminant function. The results showed that an overall discriminant accuracy of 63.04 % was confirmed. Our findings provide the possibility that daily-life sounds, beyond voice and music, can be used in a human-machine interface.

show abstract

"Of All Things the Measure Is Man" : Automatic Classification of Emotions and Inter-Labeler Consistency

Cited by 66 publications

References 3 publications

Salient Features for Anger Recognition in German and English IVR Portals

Salient Features for Anger Recognition in German and English IVR Portals

Whodunnit – Searching for the most important feature types signalling emotion-related user states in speech

Development of an auditory emotion recognition function using psychoacoustic parameters based on the International Affective Digitized Sounds

Contact Info

Product

Resources

About