Data Mining - Concepts and Techniques.

Survey on speech emotion recognition: Features, classification schemes, and databases

This survey describes recent progress in the field of Affective Computing (AC), with a focus on affect detection. Although many AC researchers have traditionally attempted to remain agnostic to the different emotion theories proposed by psychologists, the affective technologies being developed are rife with theoretical assumptions that impact their effectiveness. Hence, an informed and integrated examination of emotion theories from multiple areas will need to become part of computing practice if truly effective real-world systems are to be achieved. This survey discusses theoretical perspectives that view emotions as expressions, embodiments, outcomes of cognitive appraisal, social constructs, products of neural circuitry, and psychological interpretations of basic feelings. It provides meta-analyses on existing reviews of affect detection systems that focus on traditional affect detection modalities like physiology, face, and voice, and also reviews emerging research on more novel channels such as text, body language, and complex multimodal systems. This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations. In this way, models and methods can be compared, and emerging insights from various disciplines can be more expertly integrated.

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Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

A huge number of informal messages are posted every day in social network sites, blogs, and discussion forums. Emotions seem to be frequently important in these texts for expressing friendship, showing social support or as part of online arguments. Algorithms to identify sentiment and sentiment strength are needed to help understand the role of emotion in this informal communication and also to identify inappropriate or anomalous affective utterances, potentially associated with threatening behavior to the self or others. Nevertheless, existing sentiment detection algorithms tend to be commercially oriented, designed to identify opinions about products rather than user behaviors. This article partly fills this gap with a new algorithm, SentiStrength, to extract sentiment strength from informal English text, using new methods to exploit the de facto grammars and spelling styles of cyberspace. Applied to MySpace comments and with a lookup table of term sentiment strengths optimized by machine learning, SentiStrength is able to predict positive emotion with 60.6p accuracy and negative emotion with 72.8p accuracy, both based upon strength scales of 1–5. The former, but not the latter, is better than baseline and a wide range of general machine learning approaches. © 2010 Wiley Periodicals, Inc.

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Sentiment in short strength detection informal text

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.

Emotion recognition from text using semantic labels and separable mixture models

This paper presents an approach to emotion recognition from speech signals and textual content In the analysis of speech signals, thirty-three acoustic features are extracted from the speech input After Principle Component Analysis (PCA) is performed, 14 principle components are selected for discriminative representation In this representation, each principle component is the combination of the 33 original acoustic features and forms a feature subspace Support Vector Machines (SVMs) are adopted to classify the emotional states In text analysis, all emotional keywords and emotion modification words are manually defined The emotion intensity levels of emotional keywords and emotion modification words are estimated based on a collected emotion corpus The final emotional state is determined based on the emotion outputs from the acoustic and textual analyses Experimental results show that the emotion recognition accuracy of the integrated system is better than that of either of the two individual approaches

/pdf/multi-modal-emotion-recognition-from-speech-and-text-3g1hjqcvwk.pdf

Multi-Modal Emotion Recognition from Speech and Text

The paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-three acoustic features are extracted from the speech input. After principle component analysis (PCA), 14 principle components are selected for discriminative representation. In this representation, each principle component is the combination of the 33 original acoustic features and forms a feature subspace. Support vector machines (SVMs) are adopted to classify the emotional states. In text analysis, all emotional keywords and emotion modification words are manually defined. The emotion intensity levels of emotional keywords and emotion modification words are estimated from a collected emotion corpus. The final emotional state is determined based on the emotion outputs from the acoustic and textual approaches. The experimental result shows that the emotion recognition accuracy of the integrated system is better than each of the two individual approaches.

Emotion recognition using acoustic features and textual content

This study proposes a novel approach to the generation of video-based movement epenthesis for sign language and concentrates on a spatial interpolation approach using a nonuniform rational B-spline function to produce a smooth interpolation curve. To generate movement epenthesis, the beginning and end "cut points" are determined based on the concatenation cost, which is a linear combination of the distance, smoothness, and image distortion costs. The distance cost is defined as the normalized Euclidian distance between the palm locations at two cut points. The smoothness cost is determined by accumulating the second derivative of the curve. The image distortion cost is then estimated from the normalized Euclidian distance between the real and generated hand images. To evaluate the proposed approach, a set of sign video databases is collected and preprocessed with image calibration, content annotation, and principle component analysis. An image component overlapping procedure is also employed to yield a smooth sign video output. Evaluation results demonstrate that the synthesized trajectory is very close to the original trajectory. Moreover, the system was evaluated subjectively based on the results of ten hearing and five deaf people. The evaluation results also demonstrate the stability and feasibility of the proposed approach

Movement Epenthesis Generation Using NURBS-Based Spatial Interpolation

This paper presents an approach to feature compensation for emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are extracted first. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional states, a feature compensation approach is proposed to characterize feature space with better discriminability among emotional states. The compensation vector with respect to each emotional state is estimated using the Minimum Classification Error (MCE) algorithm. For the final emotional state decision, the compensated IG-based feature vectors are used to train the Gaussian Mixture Models (GMMs) and Continuous Support Vector Machine (CSVMs) for each emotional state. For GMMs, the emotional state with the GMM having the maximal likelihood ratio is determined as the final output. For CSVMs, the emotional state is determined according to the probability outputs from the CSVMs. The kernel function in CSVM is experimentally decided as a Radial basis function. A comparison in the experiments shows that the proposed IG-based feature compensation can obtain encouraging performance for emotion recognition.

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Ze-Jing Chuang

Papers

Emotion recognition from text using semantic labels and separable mixture models

Multi-Modal Emotion Recognition from Speech and Text

Emotion recognition using acoustic features and textual content

Movement Epenthesis Generation Using NURBS-Based Spatial Interpolation

Emotion Recognition from Speech Using IG-Based Feature Compensation