Efficient and effective strategies for cross-corpus acoustic emotion recognition

Kaya, Heysem; Karpov, Alexey

doi:10.1016/j.neucom.2017.09.049

Cited by 74 publications

(36 citation statements)

References 18 publications

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“…Interestingly, approaches in the AVEC 2018 CES did not employ approaches such as transfer learning [80,81] or domain adaptation techniques [29,54] typically seen in cross-cultural testing. In [76], the authors proposed a model based on emotional salient detection to identify emotion markers invariant to sociocultural context.…”

Section: Cross-cultural Emotion Recognitionmentioning

confidence: 99%

AVEC 2018 Workshop and Challenge

Ringeval

Schuller

Valstar

et al. 2018

Proceedings of the 2018 on Audio/Visual Emotion Challenge and Workshop

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127

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The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from reallife data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind *

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Section: Cross-cultural Emotion Recognitionmentioning

confidence: 99%

AVEC 2018 Workshop and Challenge

Ringeval

Schuller

Valstar

et al. 2018

Proceedings of the 2018 on Audio/Visual Emotion Challenge and Workshop

Self Cite

127

View full text Add to dashboard Cite

show abstract

“…This is a popular metric in the area of ASR, that to consider unweighted rather than weighted average recall. The reason is that unaffected by a change in class frequency [26,27]. As seen in Table 1, it is calculated by average between the Recall of Class 0 and the Recall of Class 1.…”

Section: Metrics For Performance Evaluationmentioning

confidence: 99%

“…The data-balancing technique adopted is based on the downsample [20], SMOTE [23] and ADASYN [24]. It is also investigated the existence or not of the "Accuracy Paradox" phenomenon, and which performance metrics should be better suited to assess classifiers, such as G-mean [25] and UAR [26,27]. For study validation, this work analyzes educational data of students from the Integrated Courses (high school with training in professional education through technical courses) updated in January 2018 for the Federal Institute of Rio Grande do Norte (IFRN), Brazil.…”

Section: Introductionmentioning

confidence: 99%

Predictive Models for Imbalanced Data: A School Dropout Perspective

et al. 2019

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Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the students who have the tendency to dropout. This problem often encounters a phenomenon that masks out the obtained results. This study delves into this phenomenon and provides a reliable educational data mining technique that accurately predicts the dropout rates. In particular, the three data classifying techniques, namely, decision tree, neural networks and Balanced Bagging, are used. The performances of these classifies are tested with and without the use of a downsample, SMOTE and ADASYN data balancing. It is found that among other parameters geometric mean and UAR provides reliable results while predicting the dropout rates using Balanced Bagging classifying techniques.

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“…In addition, a facial recognition system has been applied to evaluate the quality of distance teachings [18]. In speech analysis, emotion recognition is implemented by using the extreme learning machine (ELM) [19]. Music, in which emotions are expressed, can be analyzed to tell the difference between contemporary commercial music and classical singing techniques [20].…”

Section: Related Workmentioning

confidence: 99%

“…By using a fast Fourier transformation, the frequency features (60 power features, 16 power difference features) were prepared. In each channel, the power features were computed on four frequency bands, i.e., theta (4-8 Hz), alpha (8-12 Hz), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Power difference features were employed to detect the variation in cerebral activity between the left and right cortical areas.…”

Section: Feature Extraction and The Target Emotion Classesmentioning

confidence: 99%

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

2019

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Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.

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Efficient and effective strategies for cross-corpus acoustic emotion recognition

Cited by 74 publications

References 18 publications

AVEC 2018 Workshop and Challenge

AVEC 2018 Workshop and Challenge

Predictive Models for Imbalanced Data: A School Dropout Perspective

Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

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