Speech emotion recognition using SVM with thresholding fusion

Gupta, Shilpi; Mehra, Anu; Vinay,

doi:10.1109/spin.2015.7095427

Cited by 20 publications

(6 citation statements)

References 10 publications

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“…The SVM model, which is widely used in pattern recognition [29][30][31][32], has many unique advantages in solving small sample, nonlinear, and high-dimensional pattern recognition problems.…”

Section: Svm Modelmentioning

confidence: 99%

Speech emotion recognition based on genetic algorithm–decision tree fusion of deep and acoustic features

Sun

Qiu

et al. 2022

ETRI Journal

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Although researchers have proposed numerous techniques for speech emotion recognition, its performance remains unsatisfactory in many application scenarios. In this study, we propose a speech emotion recognition model based on a genetic algorithm (GA)-decision tree (DT) fusion of deep and acoustic features. To more comprehensively express speech emotional information, first, frame-level deep and acoustic features are extracted from a speech signal. Next, five kinds of statistic variables of these features are calculated to obtain utterance-level features. The Fisher feature selection criterion is employed to select high-performance features, removing redundant information. In the feature fusion stage, the GA is is used to adaptively search for the best feature fusion weight. Finally, using the fused feature, the proposed speech emotion recognition model based on a DT support vector machine model is realized.Experimental results on the Berlin speech emotion database and the Chinese emotion speech database indicate that the proposed model outperforms an average weight fusion method.

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Section: Svm Modelmentioning

confidence: 99%

Speech emotion recognition based on genetic algorithm–decision tree fusion of deep and acoustic features

Sun

Qiu

et al. 2022

ETRI Journal

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show abstract

“…From the computational point of view, a straightforward solution to the task of recognising humangenerated emotions is the application of machine learning techniques such as text Semantic Analysis, Naïve Bayesian Networks, Support Vector Machines, Hidden Markov Models and Fuzzy & Neural Networks, to various types of human input with emotional labels [11,32,33,38,50].…”

Section: Emotional Artificial Intelligencementioning

confidence: 99%

Emotional machines: The next revolution

Franzoni

Milani

Nardi

et al. 2019

WEB

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“…As the most direct means of human communication, voice itself can transmit abundant information. Many researchers also have conducted in-depth researches on speech emotion recognition [5][6][7][8], and good progresses have been made. Considering the internal connection between text and speech, modal fusion can be utilized to optimize the performance of the social media emotional recognition system.…”

Section: Introductionmentioning

confidence: 99%

Audio‐Textual Emotion Recognition Based on Improved Neural Networks

Cai

Dong

et al. 2019

Mathematical Problems in Engineering

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With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was resorted to capture the textual features. Furthermore, we applied a deep neural network to learn and classify the fusion features. The final emotional state was determined by the output of both speech and text emotion analysis. Finally, the multimodal fusion experiments were carried out to validate the proposed model on the IEMOCAP database. In comparison with the single modal, the overall recognition accuracy of text increased 6.70%, and that of speech emotion recognition soared 13.85%. Experimental results show that the recognition accuracy of our multimodal is higher than that of the single modal and outperforms other published multimodal models on the test datasets.

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Speech emotion recognition using SVM with thresholding fusion

Cited by 20 publications

References 10 publications

Speech emotion recognition based on genetic algorithm–decision tree fusion of deep and acoustic features

Speech emotion recognition based on genetic algorithm–decision tree fusion of deep and acoustic features

Emotional machines: The next revolution

Audio‐Textual Emotion Recognition Based on Improved Neural Networks

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