Emotion Recognition from Speech Signals Using Elicited Data and Fuzzy KDA

2013 ◽  
Vol 385-386 ◽  
pp. 1385-1388
Author(s):  
Yong Qiang Bao ◽  
Li Zhao ◽  
Cheng Wei Hang
Keyword(s):  
Discriminant Analysis ◽  
Emotion Recognition ◽  
Real World ◽  
Recognition Rate ◽  
Experimental Results ◽  
Speech Emotion Recognition ◽  
Acoustic Features ◽  
Reliable System ◽  
Kernel Discriminant Analysis ◽  
Real World Applications

In this paper we introduced the application of Fuzzy KDA in speech emotion recognition using elicited data. The emotional data induced in a psychology experiment. The acted data is not suitable for developing real world applications and by using more naturalistic data we may build more reliable system. The emotional feature set is then constructed for modeling and recognition. A total of 372 low level acoustic features are used and kernel discriminant analysis is used for emotion recognition. The experimental results show a promising recognition rate.

Speech Emotion Recognition Using Multiple Discriminant Analysis and Gaussian Mixture Model

2013 ◽  
Vol 380-384 ◽  
pp. 3530-3533
Author(s):  
Yong Qiang Bao ◽  
Li Zhao ◽  
Cheng Wei Huang
Keyword(s):  
Discriminant Analysis ◽  
Emotion Recognition ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Speech Signal ◽  
Recognition Rate ◽  
Gaussian Mixture ◽  
Pitch Contour ◽  
Speech Emotion Recognition ◽  
Multiple Discriminant Analysis

In this paper we studied speech emotion recognition from Mandarin speech signal. Five basic emotion classes and the neutral state are considered. In a listening experiment we verified the speech corpus using a judgment matrix. Acoustic parameters including short-term energy, pitch contour, and formants are extracted from emotional speech signal. Gaussian mixture model is then adopted for training the emotion model. Due to the data challenge in GMM training, we use multiple discriminant analysis for feature optimization and compared with basic Fisher discriminant ratio based method. The experimental results show that using multiple discriminant analysis our GMM classifier gives a promising recognition rate for Mandarin speech emotion recognition.

scholarly journals A New No Equilibrium Fractional Order Chaotic System, Dynamical Investigation, Synchronization, and Its Digital Implementation

Inventions ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 49
Author(s):  
Zain-Aldeen S. A. Rahman ◽  
Basil H. Jasim ◽  
Yasir I. A. Al-Yasir ◽  
Raed A. Abd-Alhameed ◽  
Bilal Naji Alhasnawi
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Chaotic System ◽  
Real World ◽  
Experimental Results ◽  
Chaotic Attractors ◽  
State Variables ◽  
Digital Implementation ◽  
Dynamical Behaviors ◽  
Real World Applications

In this paper, a new fractional order chaotic system without equilibrium is proposed, analytically and numerically investigated, and numerically and experimentally tested. The analytical and numerical investigations were used to describe the system’s dynamical behaviors including the system equilibria, the chaotic attractors, the bifurcation diagrams, and the Lyapunov exponents. Based on the obtained dynamical behaviors, the system can excite hidden chaotic attractors since it has no equilibrium. Then, a synchronization mechanism based on the adaptive control theory was developed between two identical new systems (master and slave). The adaptive control laws are derived based on synchronization error dynamics of the state variables for the master and slave. Consequently, the update laws of the slave parameters are obtained, where the slave parameters are assumed to be uncertain and are estimated corresponding to the master parameters by the synchronization process. Furthermore, Arduino Due boards were used to implement the proposed system in order to demonstrate its practicality in real-world applications. The simulation experimental results were obtained by MATLAB and the Arduino Due boards, respectively, with a good consistency between the simulation results and the experimental results, indicating that the new fractional order chaotic system is capable of being employed in real-world applications.

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.

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%.

Important Attributes Selection Based on Rough Set for Speech Emotion Recognition

2011 ◽  
pp. 262-271
Author(s):  
Jian Zhou ◽  
Guoyin Wang ◽  
Yong Yang
Keyword(s):  
Emotion Recognition ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Recognition Rate ◽  
Feature Selection Method ◽  
Recognition System ◽  
Attribute Selection ◽  
Computer Application ◽  
Speech Emotion Recognition

Speech emotion recognition is becoming more and more important in such computer application fields as health care, children education, etc. In order to improve the prediction performance or providing faster and more cost-effective recognition system, an attribute selection is often carried out beforehand to select the important attributes from the input attribute sets. However, it is time-consuming for traditional feature selection method used in speech emotion recognition to determine an optimum or suboptimum feature subset. Rough set theory offers an alternative, formal and methodology that can be employed to reduce the dimensionality of data. The purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying important features in speech emotion recognition system. The experiments on CLDC emotion speech database clearly show this approach can reduce the calculation cost while retaining a suitable high recognition rate.

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.

Attenuation of Acoustic Emission Signals in Structural Interfaces

2012 ◽  
Vol 569 ◽  
pp. 343-346
Author(s):  
Xiang Hong Wang ◽  
Hong Wei Hu ◽  
Zhi Yong Zhang
Keyword(s):  
Acoustic Emission ◽  
Real World ◽  
Simple Structure ◽  
Size Structure ◽  
Experimental Results ◽  
Signal Energy ◽  
Real World Applications ◽  
Structural Interfaces ◽  
Ae Signals

Received acoustic emission (AE) signals are transmitted across structural interfaces in many real-world applications. This paper studies attenuation of the signals across two common structural interfaces. The experimental results indicate that interface has effects on attenuation, which depends on the relative scales of structures. Signal energy is strengthened due to multiple flections of signals on the small-size structure when an interface is constructed by different scales. Thus the received signals are distorted worse than the original signals. So it is a better way to mount sensors on a simple structure with a size as much as a structure incurred AE sources.

Research on Recognition of Pathological Voice by AD Tree

2013 ◽  
Vol 658 ◽  
pp. 647-651 ◽  
Author(s):  
Jun Jie Zhu ◽  
Xiao Jun Zhang ◽  
Ji Hua Gu ◽  
He Ming Zhao ◽  
Qiang Zhou ◽  
...  
Keyword(s):  
Cross Validation ◽  
Recognition Rate ◽  
Experimental Results ◽  
Acoustic Features ◽  
Recognition Process ◽  
Normal Voice ◽  
The Cross ◽  
Fold Cross Validation ◽  
Pathological Voice

This paper mainly studies on the classification of pathological voice from normal voice based on the sustained vowel /a/. Firstly, the original 18 acoustic features are extracted. Then on the basis of the extracted parameters, this paper recognizes the pathological voice using AD Tree. During the classification stage, the cross-validation of features is also as references in the process. This method is validated with a sound database provided by the Massachusetts Eye and Ear Infirmary (MEEI). After the 10 fold cross-validation, comparing with 7 other kinds of classifiers, the experimental results show that AD Tree can get the highest recognition rate of 95.2%. The method in this paper shows that all the extracted parameters are reasonable in the following recognition process and AD tree is a good recognition way in pathological voice research.

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