Performance analysis of spectral and prosodic features and their fusion for emotion recognition in speech

Gaurav, M.

doi:10.1109/slt.2008.4777903

Cited by 9 publications

(2 citation statements)

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“…Looking at the 'opposite side' of interest, i. e., boredom, there exist several studies recognising it in line with other prototypical emotion categories; however, they usually employ acted data. Probably the most frequently used (acted) data set is the Berlin Emotional Speech database (Burkhardt et al, 2005a)-for results on this set cf., e. g., (Schuller et al, 2010c;Gaurav, 2008). Besides this set, others exist, such as the one used in (Pao et al, 2010).…”

Section: Historical Overviewmentioning

confidence: 99%

Paralinguistics in speech and language—State-of-the-art and the challenge

Schuller

Steidl

Batliner

et al. 2013

Computer Speech & Language

262

159

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Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. This article aims to provide a broad overview of the constantly growing field by defining the field, introducing typical applications, presenting exemplary resources, and sharing a unified view of the chain of processing. It then presents the first broader Paralinguistic Challenge organised at INTERSPEECH 2010 by the authors including a historical overview of the Challenge tasks of recognising age, gender, and affect, a summary of methods used by the participants, and their results. In addition, we present the new benchmark obtained by fusion of participants' predictions and conclude by discussing ten recent and emerging trends in the analysis of paralinguistics in speech and language.

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Section: Historical Overviewmentioning

confidence: 99%

Paralinguistics in speech and language—State-of-the-art and the challenge

Schuller

Steidl

Batliner

et al. 2013

Computer Speech & Language

262

159

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“…In some publications scientists compare effects of several different classifiers. Gaurav [23] has done a comparison between k-NN and Support Vector Machines.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Human Emotion from a Speech Signal Based on Plutchik's Model

Kamińska¹,

Pelikant²

2012

International Journal of Electronics and Telecommunications

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Recognition of Human Emotion from a Speech Signal Based on Plutchik's ModelMachine recognition of human emotional states is an essential part in improving man-machine interaction. During expressive speech the voice conveys semantic message as well as the information about emotional state of the speaker. The pitch contour is one of the most significant properties of speech, which is affected by the emotional state. Therefore pitch features have been commonly used in systems for automatic emotion detection. In this work different intensities of emotions and their influence on pitch features have been studied. This understanding is important to develop such a system. Intensities of emotions are presented on Plutchik's cone-shaped 3D model. ThekNearest Neighbor algorithm has been used for classification. The classification has been divided into two parts. First, the primary emotion has been detected, then its intensity has been specified. The results show that the recognition accuracy of the system is over 50% for primary emotions, and over 70% for its intensities.

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