scholarly journals Multi-Layer Hybrid Fuzzy Classification Based on SVM and Improved PSO for Speech Emotion Recognition

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2891
Author(s):  
Shihan Huang ◽  
Hua Dang ◽  
Rongkun Jiang ◽  
Yue Hao ◽  
Chengbo Xue ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Fuzzy Classification ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Fuzzy C Means ◽  
Svm Model ◽  
Wide Range

Speech Emotion Recognition (SER) plays a significant role in the field of Human–Computer Interaction (HCI) with a wide range of applications. However, there are still some issues in practical application. One of the issues is the difference between emotional expression amongst various individuals, and another is that some indistinguishable emotions may reduce the stability of the SER system. In this paper, we propose a multi-layer hybrid fuzzy support vector machine (MLHF-SVM) model, which includes three layers: feature extraction layer, pre-classification layer, and classification layer. The MLHF-SVM model solves the above-mentioned issues by fuzzy c-means (FCM) based on identification information of human and multi-layer SVM classifiers, respectively. In addition, to overcome the weakness that FCM tends to fall into local minima, an improved natural exponential inertia weight particle swarm optimization (IEPSO) algorithm is proposed and integrated with fuzzy c-means for optimization. Moreover, in the feature extraction layer, non-personalized features and personalized features are combined to improve accuracy. In order to verify the effectiveness of the proposed model, all emotions in three popular datasets are used for simulation. The results show that this model can effectively improve the success rate of classification and the maximum value of a single emotion recognition rate is 97.67% on the EmoDB dataset.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

2016 ◽  
Vol 7 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotional States ◽  
Wide Range ◽  
Leibler Divergence ◽  
Perceptual Linear Prediction

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

Speech Emotion Feature Extraction Using FFT Spectrum Analysis

2015 ◽  
Vol 781 ◽  
pp. 551-554 ◽  
Author(s):  
Chaidiaw Thiangtham ◽  
Jakkree Srinonchat
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Performance ◽  
Random Noise ◽  
Gaussian Mixture ◽  
Learning System ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotion Classification ◽  
Zero Crossing

Speech Emotion Recognition has widely researched and applied to some appllication such as for communication with robot, E-learning system and emergency call etc.Speech emotion feature extraction is an importance key to achieve the speech emotion recognition which can be classify for personal identity. Speech emotion features are extracted into several coefficients such as Linear Predictive Coefficients (LPCs), Linear Spectral Frequency (LSF), Zero-Crossing (ZC), Mel-Frequency Cepstrum Coefficients (MFCC) [1-6] etc. There are some of research works which have been done in the speech emotion recgnition. A study of zero-crossing with peak-amplitudes in speech emotion classification is introduced in [4]. The results shown that it provides the the technique to extract the emotion feature in time-domain, which still got the problem in amplitude shifting. The emotion recognition from speech is descrpited in [5]. It used the Gaussian Mixture Model (GMM) for extractor of feature speech. The GMM is provided the good results to reduce the back ground noise, howere it still have to focus on random noise in GMM for recognition model. The speech emotion recognition using hidden markov model and support vector machine is explained in [6]. The results shown the average performance of recognition system according to the features of speech emotion still has got the error information. Thus [1-6] provides the recognition performance which still requiers more focus on speech features.

Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition

2020 ◽  
pp. 283-293
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Recognition Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Emotional States ◽  
Perceptual Linear Prediction

Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

Speech emotion recognition based on DNN-decision tree SVM model

2019 ◽  
Vol 115 ◽  
pp. 29-37 ◽  
Author(s):  
Linhui Sun ◽  
Bo Zou ◽  
Sheng Fu ◽  
Jia Chen ◽  
Fu Wang
Keyword(s):  
Decision Tree ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Svm Model

Speech Emotional Features Extraction Based on Electroglottograph

2013 ◽  
Vol 25 (12) ◽  
pp. 3294-3317 ◽  
Author(s):  
Lijiang Chen ◽  
Xia Mao ◽  
Pengfei Wei ◽  
Angelo Compare
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Vocal Tract ◽  
Vocal Folds ◽  
Distribution Coefficients ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Power Law Distribution ◽  
Transform Coefficients ◽  
Better Than

This study proposes two classes of speech emotional features extracted from electroglottography (EGG) and speech signal. The power-law distribution coefficients (PLDC) of voiced segments duration, pitch rise duration, and pitch down duration are obtained to reflect the information of vocal folds excitation. The real discrete cosine transform coefficients of the normalized spectrum of EGG and speech signal are calculated to reflect the information of vocal tract modulation. Two experiments are carried out. One is of proposed features and traditional features based on sequential forward floating search and sequential backward floating search. The other is the comparative emotion recognition based on support vector machine. The results show that proposed features are better than those commonly used in the case of speaker-independent and content-independent speech emotion recognition.

A Mathematical Morphological Processing of Spectrograms for the Tone of Chinese Vowels Recognition

2014 ◽  
Vol 571-572 ◽  
pp. 665-671 ◽  
Author(s):  
Sen Xu ◽  
Xu Zhao ◽  
Cheng Hua Duan ◽  
Xiao Lin Cao ◽  
Hui Yan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Morphological Processing ◽  
Speech Emotion Recognition ◽  
Normal Tone ◽  
Tone Recognition ◽  
Tone Signal ◽  
The Neural Networks

As One of Features from other Languages, the Chinese Tone Changes of Chinese are Mainly Decided by its Vowels, so the Vowel Variation of Chinese Tone Becomes Important in Speech Recognition Research. the Normal Tone Recognition Ways are Always Based on Fundamental Frequency of Signal, which can Not Keep Integrity of Tone Signal. we Bring Forward to a Mathematical Morphological Processing of Spectrograms for the Tone of Chinese Vowels. Firstly, we will have Pretreatment to Recording Good Tone Signal by Using Cooledit Pro Software, and Converted into Spectrograms; Secondly, we will do Smooth and the Normalized Pretreatment to Spectrograms by Mathematical Morphological Processing; Finally, we get Whole Direction Angle Statistics of Tone Signal by Skeletonization way. the Neural Networks Stimulation Shows that the Speech Emotion Recognition Rate can Reach 92.50%.

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

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