Automatic Recognition of Emotional States From Human Speeches

Cen, Ling; Ser, Wee; Yu, Zhu Liang; Cen, Wei

doi:10.5772/9371

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…There are some literatures on this topic. For example, refer to the papers [1][2][3][4][5][6]. In the paper [1], the Fuzzy Rank-Based Ensemble of Transfer Learning Model is used for speech emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

“…A hybrid system of using signals from faces and voices to recognize people's emotions is proposed in the paper [4]. An exploration of various models and speech features for speech emotion recognition is introduced in papers [5,6]. However, according to the studies [7,8], the main topics of automatic emotion recognition from speeches include the selection of a database, feature extraction problems, and development of recognition algorithms [8].…”

Section: Introductionmentioning

confidence: 99%

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Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decomposition and Deep Neural Network

Pan¹,

Chen

Hong

2022

BIJIAM

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This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decomposition and Deep Neural Network

Pan¹,

Chen

Hong

2022

BIJIAM

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“…The first method uses facial expression and has been developed over more than two decades, see, for example, [1][2][3]. In recent years, emotion recognition by speech has emerged, see, for example, [4][5][6][7] and the references therein. However, these two methods are subject to interference by environmental noise, reducing recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

Pan

2020

Asian Journal of Control

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In this paper, a Fuzzy Hidden Markov Model (FHMM) for electrocardiogram (ECG)-based emotion recognition is proposed. The FHMM model is modeled using the fuzzy membership of each class of feature vectors to compute the elements of the matric in the model. Each element in the matric is determined by the two nearest classes of feature vectors with fuzzy classification to avoid the winner-takes-all situation that usually happens in tradition discrete Hidden Markov Models (HMM) and that reduces the accuracy of the modeling results. The FHMM modeling proposed in this paper can improve the precision of traditional discrete HMM modeling. Also, the features for emotion recognition, which are selected according to those used in prior research, are calculated from ECG signals. The selected features form a feature vector and these are calculated for all ECG signals. Some feature vectors are used to train the FHMM model and the remaining are used for testing. Experimental results show that the proposed FHMM model can achieve impressive improvements on the three indices, sensitivities (Se%), positive predictive value (PPV%), and total classification accuracy (TCA%), compared to traditional HMM. Moreover, compared to some existing studies, the recognition rates of the proposed method are higher. These results verify the efficiency of the proposed method for emotion recognition from ECG signals.

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“…The exploration of emotion recognition from speeches then becomes more important. Although this topic is a new research area, there are already some literatures about this topic, see for example the papers (Schuller, Rigoll & Lang 2003, Fragopanagos & Taylor 2005, Cen et al 2010, Wu, Falk & Chan 2011, Petrusihin 2000, Tato et al 2002, Yacoub et al 2003, Schuller, Rigoll & Lang 2004, New, Foo & Silva 2003 and the references therein. However, in the existing literatures, the emotion recognition rate from speech is low and is far away from practical applications.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Speech Signals by Using Evolutionary Algorithm and Empirical Mode Decomposition

Pan

Ouyang

et al. 2018

Electronic Workshops in Computing

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Emotion status impacts massively the human's health and the job performance of people. A system that can continuously and automatically monitor people's emotion is worthy of development. Besides, the speech signals always contain some emotion features and are the most commonly used for communication between humans. The exploration of emotion recognition from speeches then becomes more important. In this paper, we propose a strategy for emotion recognition from speech by combining evolutionary algorithm (EA) with Empirical Mode Decomposition (EMD) to improve the emotion recognition rate. First, some emotional speeches were decomposed into several Intrinsic Mode Functions (IMFs) by using EMD process. The emotional part of a speech is then extracted by using these IMFs. In this paper, some weighted IMFs obtained from EMD are combined for the following recognition process. Hence, it is one of the goals of this paper to find the optimal weights corresponding to each IMF and to combine these weighted IMFs to make the recognition results as accurate as possible. The weights for each IMF are trained by evolutionary algorithm to find an optimal combination of IMFs. The reason why evolutional algorithm is used here is that evolutional algorithm always obtains some outstanding performances in many research concerning optimal design. The Mel-Frequency Cepstral Coefficients (MFCCs) are then computed and are used as the features for emotion recognition. An open database, eNTERFACE 2005 emotion database, is adopted in this paper as training and testing data for the experiments.Emotion recognition. Speech signals. Empirical mode decomposition. Genetic algorithm.

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Automatic Recognition of Emotional States From Human Speeches

Cited by 7 publications

References 30 publications

Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decomposition and Deep Neural Network

Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decomposition and Deep Neural Network

Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

Emotion Recognition from Speech Signals by Using Evolutionary Algorithm and Empirical Mode Decomposition

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