Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech

Demircan, Semiye; Kahramanlı, Humar

doi:10.1007/s00521-016-2712-y

Cited by 46 publications

(21 citation statements)

References 34 publications

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“…Demircan et al proposed speech recognition system based on Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) and used fuzzy c-means for dimensionality reduction. They reported a peak classification accuracy of 92.86% for seven emotions for German language dataset, EmoDB using SVM classifier [26].…”

Section: Discussionmentioning

confidence: 99%

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Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Krishnan

Raj

Rajangam

2021

Complex Intell. Syst.

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Emotion recognition system from speech signal is a widely researched topic in the design of the Human–Computer Interface (HCI) models, since it provides insights into the mental states of human beings. Often, it is required to identify the emotional condition of the humans as cognitive feedback in the HCI. In this paper, an attempt to recognize seven emotional states from speech signals, known as sad, angry, disgust, happy, surprise, pleasant, and neutral sentiment, is investigated. The proposed method employs a non-linear signal quantifying method based on randomness measure, known as the entropy feature, for the detection of emotions. Initially, the speech signals are decomposed into Intrinsic Mode Function (IMF), where the IMF signals are divided into dominant frequency bands such as the high frequency, mid-frequency , and base frequency. The entropy measures are computed directly from the high-frequency band in the IMF domain. However, for the mid- and base-band frequencies, the IMFs are averaged and their entropy measures are computed. A feature vector is formed from the computed entropy measures incorporating the randomness feature for all the emotional signals. Then, the feature vector is used to train a few state-of-the-art classifiers, such as Linear Discriminant Analysis (LDA), Naïve Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Gradient Boosting Machine. A tenfold cross-validation, performed on a publicly available Toronto Emotional Speech dataset, illustrates that the LDA classifier presents a peak balanced accuracy of 93.3%, F1 score of 87.9%, and an area under the curve value of 0.995 in the recognition of emotions from speech signals of native English speakers.

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Section: Discussionmentioning

confidence: 99%

“…Later, they used Fuzzy c-means for feature dimension reduction which was further given as input to machine learning classifiers. They used German speech emotion dataset for their work [26].…”

Section: Introductionmentioning

confidence: 99%

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Krishnan

Raj

Rajangam

2021

Complex Intell. Syst.

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“…Several classifiers such as ANN, NB, KNN, and SVM have been used for classification emotions from speech. The highest classification rate (92.86%) was obtained by KNN and SVM classifiers [58].…”

Section: Related Workmentioning

confidence: 97%

“…The compared classifiers [20] KNN and SVM [44] GMM, ANN, VQ and K-means clustering [52] FLDA, GMM, ANN, KNN and MLC [36] GMM-DNN, SVM and MLP [53] KNN and GMM [54] SVM, NN, RF and NB [55] SVM, MLP and KNN [56] LDA, RDA, SVM and KNN [47] GMM and SVM [57] RNN and SVM [58] KNN, SVM, ANN and NB…”

Section: The Workmentioning

confidence: 99%

Recognition of emotions in the Algerian Dialect Speech

Horkous¹,

Guerti²

2021

IJCDS

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“…Many papers focused on learning methods of machine learning or deep learning. Demircan [17] introduced fuzzy c-mean as a preprocessing step to group and add characteristics before using machine learning. Venkataramanan [21] studied of using deep learning in SER.…”

Section: Literature Reviewsmentioning

confidence: 99%

Deep Residual Local Feature Learning for Speech Emotion Recognition

Singkul

Chatchaisathaporn²,

Suntisrivaraporn³

et al. 2020

Neural Information Processing

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Speech Emotion Recognition (SER) is becoming a key role in global business today to improve service efficiency, like call center services. Recent SERs were based on a deep learning approach. However, the efficiency of deep learning depends on the number of layers, i.e., the deeper layers, the higher efficiency. On the other hand, the deeper layers are causes of a vanishing gradient problem, a low learning rate, and high time-consuming. Therefore, this paper proposed a redesign of existing local feature learning block (LFLB). The new design is called a deep residual local feature learning block (DeepResLFLB). DeepResLFLB consists of three cascade blocks: LFLB, residual local feature learning block (ResLFLB), and multilayer perceptron (MLP). LFLB is built for learning local correlations along with extracting hierarchical correlations; DeepResLFLB can take advantage of repeatedly learning to explain more detail in deeper layers using residual learning for solving vanishing gradient and reducing overfitting; and MLP is adopted to find the relationship of learning and discover probability for predicted speech emotions and gender types. Based on two available published datasets: EMODB•and RAVDESS, the proposed DeepResLFLB can significantly improve performance when evaluated by standard metrics: accuracy, precision, recall, and F1-score.

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Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech

Cited by 46 publications

References 34 publications

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Emotion classification from speech signal based on empirical mode decomposition and non-linear features

Recognition of emotions in the Algerian Dialect Speech

Deep Residual Local Feature Learning for Speech Emotion Recognition

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