A Novel Feature for Emotion Recognition in Voice Based Applications

Maganti, Hari Krishna; Scherer, Stefan; Palm, Günther

doi:10.1007/978-3-540-74889-2_62

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…2. The biologically inspired Modulation spectrum (ModSpec) based features reflect the variations in the frequencies of the signal and are extracted from 200ms sized windows with a sample rate of 50Hz [10,6]. 3.…”

Section: Audio Features and Hmm Sequence Encodingmentioning

confidence: 99%

Multimodal Emotion Classification in Naturalistic User Behavior

Walter

Scherer

Schels

et al. 2011

Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments

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The design of intelligent personalized interactive systems, having knowledge about the user's state, his desires, needs and wishes, currently poses a great challenge to computer scientists. In this study we propose an information fusion approach combining acoustic, and biophysiological data, comprising multiple sensors, to classify emotional states. For this purpose a multimodal corpus has been created, where subjects undergo a controlled emotion eliciting experiment, passing several octants of the valence arousal dominance space. The temporal and decision level fusion of the multiple modalities outperforms the single modality classifiers and shows promising results.

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Section: Audio Features and Hmm Sequence Encodingmentioning

confidence: 99%

Multimodal Emotion Classification in Naturalistic User Behavior

Walter

Scherer

Schels

et al. 2011

Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments

Self Cite

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“…These features have been used in previous experiments and tasks such as emotion recognition and laughter detection [Scherer et al 2008a;Truong and Van Leeuwen 2005;Maganti et al 2007]. The features are extracted using standard methods like Mel filtering and Fast Fourier Transformations (FFT).…”

Section: Audio-based Features: Modulation Spectrum Featuresmentioning

confidence: 99%

Spotting laughter in natural multiparty conversations

Scherer

Glodek²,

Schwenker³

et al. 2012

ACM Trans. Interact. Intell. Syst.

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It is essential for the advancement of human-centered multimodal interfaces to be able to infer the current user's state or communication state. In order to enable a system to do that, the recognition and interpretation of multimodal social signals (i.e., paralinguistic and nonverbal behavior) in real-time applications is required. Since we believe that laughs are one of the most important and widely understood social nonverbal signals indicating affect and discourse quality, we focus in this work on the detection of laughter in natural multiparty discourses. The conversations are recorded in a natural environment without any specific constraint on the discourses using unobtrusive recording devices. This setup ensures natural and unbiased behavior, which is one of the main foci of this work. To compare results of methods, namely Gaussian Mixture Model (GMM) supervectors as input to a Support Vector Machine (SVM), so-called Echo State Networks (ESN), and a Hidden Markov Model (HMM) approach, are utilized in online and offline detection experiments. The SVM approach proves very accurate in the offline classification task, but is outperformed by the ESN and HMM approach in the online detection (F 1 scores: GMM SVM 0.45, ESN 0.63, HMM 0.72). Further, we were able to utilize the proposed HMM approach in a cross-corpus experiment without any retraining with respectable generalization capability (F 1 score: 0.49). The results and possible reasons for these outcomes are shown and discussed in the article. The proposed methods may be directly utilized in practical tasks such as the labeling or the online detection of laughter in conversational data and affect-aware applications.

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Emotion Recognition from Speech

Wendemuth

Vlasenko

Siegert

et al. 2017

Cognitive Technologies

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an emotion is a mental and physiological state associated with a wide variety of feelings, thoughts, and behavior. Emotions are subjective experiences, or experienced from an individual point of view. Emotion is often associated with mood, temperament, personality, and disposition. Hence, in this paper method for detection of human emotions is discussed based on the acoustic features like pitch, energy etc. The proposed system is using the traditional MFCC approach [2] and then using nearest neighbor algorithm for the classification. Emotions has been classified separately for male and female based on the fact male and female voice has altogether different range [1][4] so MFCC varies considerably for the two.

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A Novel Feature for Emotion Recognition in Voice Based Applications

Cited by 9 publications

References 3 publications

Multimodal Emotion Classification in Naturalistic User Behavior

Multimodal Emotion Classification in Naturalistic User Behavior

Spotting laughter in natural multiparty conversations

Emotion Recognition from Speech

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