Prosody based emotion recognition for MEXI

Austermann, Anja; Esau, Natascha; Kleinjohann, Lisa; Kleinjohann, Bernd

doi:10.1109/iros.2005.1545341

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…Previous work [2] has indicated that an average emotion recognition rate of 84% is achieved in speaker-dependent experiments, whereas for the speaker-independent case the emotion recognition drops to 60%. The aforementioned conclusion is also verified in [56] for 10 different classifiers.…”

Section: Speaker-independent Experimental Protocolmentioning

confidence: 86%

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Kotti

Paternò

2012

Int J Speech Technol

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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, Fujisaki's 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.

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Section: Speaker-independent Experimental Protocolmentioning

confidence: 86%

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Kotti

Paternò

2012

Int J Speech Technol

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“…Most of the existing systems for automatic vocal affect recognition were trained and tested on speech data that was collected by asking actors to speak prescribed utterances with certain emotions (e.g., [6] and [79]). As the utterances are isolated from the interaction context, this experimental strategy precludes finding and using correlations between the paralinguistic displays and the linguistic content, which seem to play an important role for affect recognition in daily interpersonal interactions.…”

Section: Audio-based Affect Recognitionmentioning

confidence: 99%

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Zeng

Pantić

Roisman

et al. 2009

IEEE Trans. Pattern Anal. Mach. Intell.

2,260

227

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Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.

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“…The popular features are prosodic features (e.g., pitch-related features, energy-related features, and speech rate) and spectral features (e.g., mel frequency cepstral coefficients (MFCCs) and cepstral features). Most of the existing approaches are trained and tested on speech data that were collected by asking actors to speak prescribed utterances with certain emotions [10], [13]. However, the fact 1520-9210/$26.00 © 2010 IEEE that deliberate behavior differs in audio profile and timing from spontaneous behavior has led research to shift towards the analysis of spontaneous human behavior in naturalistic audio recordings.…”

Section: Related Research and Motivationmentioning

confidence: 99%

Speech Emotion Analysis: Exploring the Role of Context

Tawari

Trivedi

2010

IEEE Trans. Multimedia

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Abstract-Automated analysis of human affective behavior has attracted increasing attention in recent years. With the research shift toward spontaneous behavior, many challenges have come to surface ranging from database collection strategies to the use of new feature sets (e.g., lexical cues apart from prosodic features). Use of contextual information, however, is rarely addressed in the field of affect expression recognition, yet it is evident that affect recognition by human is largely influenced by the context information. Our contribution in this paper is threefold. First, we introduce a novel set of features based on cepstrum analysis of pitch and intensity contours. We evaluate the usefulness of these features on two different databases: Berlin Database of emotional speech (EMO-DB) and locally collected audiovisual database in car settings (CVRRCar-AVDB). The overall recognition accuracy achieved for seven emotions in the EMO-DB database is over 84% and over 87% for three emotion classes in CVRRCar-AVDB. This is based on tenfold stratified cross validation. Second, we introduce the collection of a new audiovisual database in an automobile setting (CVRRCar-AVDB). In this current study, we only use the audio channel of the database. Third, we systematically analyze the effects of different contexts on two different databases. We present context analysis of subject and text based on speaker/text-dependent/-independent analysis on EMO-DB. Furthermore, we perform context analysis based on gender information on EMO-DB and CVRRCar-AVDB. The results based on these analyses are promising.

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Prosody based emotion recognition for MEXI

Cited by 17 publications

References 11 publications

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Speech Emotion Analysis: Exploring the Role of Context

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