IITKGP-SESC: Speech Database for Emotion Analysis

Koolagudi, Shashidhar G.; Maity, Sudhamay; Kumar, Vuppala Anil; Chakrabarti, Saswat; Rao, K. Sreenivasa

doi:10.1007/978-3-642-03547-0_46

Cited by 94 publications

(30 citation statements)

References 4 publications

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“…They are also known as underlying emotions. Naturally available real world data may be recorded from call center conversations, cockpit recordings , Emo-DB (Burkhardt et al 2005), IITKGP-SESC (Koolagudi et al 2009). …”

Section: Databases: a Reviewmentioning

confidence: 99%

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Emotion recognition from speech: a review

Koolagudi

Rao

2012

Int J Speech Technol

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486

177

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Recognizing emotions from speech is a tuff task as we are not aware of the features which will accurately classify the emotions. This paper is an approach to show which speech feature classifies the emotions more accurately. The features compared here are Pitch and Formant while the classifier used is Linear Discriminant Analysis (LDA). The database used in this experiment was developed using 50 male and 50 female Marathi speaking native speakers. The emotions used here are Neutral, Happy, Sad, Surprise and Boredom. At the end of the experiment it was observed that formant recognized the emotions very efficiently and accurately with respect to that of energy.

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Section: Databases: a Reviewmentioning

confidence: 99%

“…It is particularly useful for enhancing naturalness in speech based human machine interaction (Schuller et al 2004;Dellert et al 1996;Koolagudi et al 2009). Emotion recognition system may be used in an onboard car driving system, where information about mental state of a driver may be used to keep him alert during driving.…”

Section: Introductionmentioning

confidence: 99%

Emotion recognition from speech: a review

Koolagudi

Rao

2012

Int J Speech Technol

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486

177

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“…The IIT-KGP SESC database in Telugu language [74] is used in this study. From this database, utterances of 5 emotions (neutral, anger, happiness, sadness and fear) of the same sentence of two speakers are considered.…”

Section: Analysis-by-synthesis To Explore Relative Contribution Of DImentioning

confidence: 99%

“…In this Section, the overview of these features is given along with the literature of emotion recognition studies. • LDC [138] • IEMOCAP [18] • VAM [51] • EMO-DB [17] • Belfast [116] • Call centers [77,87] • DES [28] • NIMITEK [44] • AIBO [117] • IITKGP-SESC [74] Advantages…”

Section: Emotional Speech Analysismentioning

confidence: 99%

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Analysis of Emotional Speech—A Review

Gangamohan

Kadiri

Yegnanarayana

2016

Toward Robotic Socially Believable Behaving Systems - Volume I

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Speech carries information not only about the lexical content, but also about the age, gender, signature and emotional state of the speaker. Speech in different emotional states is accompanied by distinct changes in the production mechanism. In this chapter, we present a review of analysis methods used for emotional speech. In particular, we focus on the issues in data collection, feature representations and development of automatic emotion recognition systems. The significance of the excitation source component of speech production in emotional states is examined in detail. The derived excitation source features are shown to carry the emotion correlates. IntroductionHumans have evolved various forms of communication like facial expressions, gestures, body postures, speech, etc. The form of communication depends on the context of interaction, and is often accompanied by various physiological reactions such as changes in the heart rate, skin resistance, temperature, muscle activity and blood pressure. All forms of human communication carry information at two levels, the message and the underlying emotional state.Emotions are essential part of real life communication among human beings. Various descriptions of the term emotion are studied in [21,22,60,88,92,98,100]. Some of the descriptions are: (a) "Emotions are underlying states which are evolved and adaptive. Emotion expressions are produced by the communicative value of underlying states" [22].

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Introduction

Krothapalli

Koolagudi

2012

SpringerBriefs in Electrical and Computer Engineering

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IITKGP-SESC: Speech Database for Emotion Analysis

Cited by 94 publications

References 4 publications

Emotion recognition from speech: a review

Emotion recognition from speech: a review

Analysis of Emotional Speech—A Review

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