scholarly journals Speech Emotion Recognition System Using Gaussian Mixture Model and Improvement proposed via Boosted GMM

2017 ◽  
Vol 7 (2 (S)) ◽  
pp. 56
Author(s):  
Pavitra Patel ◽  
A. A. Chaudhari ◽  
M. A. Pund ◽  
D. H. Deshmukh
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Training Data ◽  
Speech Emotion Recognition ◽  
Human Beings ◽  
Human Machine Interaction ◽  
Data Set ◽  
Communication Partner

<p>Speech emotion recognition is an important issue which affects the human machine interaction. Automatic recognition of human emotion in speech aims at recognizing the underlying emotional state of a speaker from the speech signal. Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the expectation maximization (EM) algorithm based on a training data set. In this paper, we introduce a boosting algorithm for reliably and accurately estimating the class-conditional GMMs. The resulting algorithm is named the Boosted-GMM algorithm. Our speech emotion recognition experiments show that the emotion recognition rates are effectively and significantly boosted by the Boosted-GMM algorithm as compared to the EM-GMM algorithm.<br />During this interaction, human beings have some feelings that they want to convey to their communication partner with whom they are communicating, and then their communication partner may be the human or machine. This work dependent on the emotion recognition of the human beings from their speech signal<br />Emotion recognition from the speaker’s speech is very difficult because of the following reasons: Because of the existence of the different sentences, speakers, speaking styles, speaking rates accosting variability was introduced. The same utterance may show different emotions. Therefore it is very difficult to differentiate these portions of utterance. Another problem is that emotion expression is depending on the speaker and his or her culture and environment. As the culture and environment gets change the speaking style also gets change, which is another challenge in front of the speech emotion recognition system.</p>

scholarly journals Practical Speech Emotion Recognition Based on Online Learning: From Acted Data to Elicited Data

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chengwei Huang ◽  
Ruiyu Liang ◽  
Qingyun Wang ◽  
Ji Xi ◽  
Cheng Zha ◽  
...  
Keyword(s):  
Online Learning ◽  
Emotion Recognition ◽  
Cognitive Task ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Independent Data ◽  
Data Set ◽  
Speaker Independent ◽  
Maximal Information Coefficient

We study the cross-database speech emotion recognition based on online learning. How to apply a classifier trained on acted data to naturalistic data, such as elicited data, remains a major challenge in today’s speech emotion recognition system. We introduce three types of different data sources: first, a basic speech emotion dataset which is collected from acted speech by professional actors and actresses; second, a speaker-independent data set which contains a large number of speakers; third, an elicited speech data set collected from a cognitive task. Acoustic features are extracted from emotional utterances and evaluated by using maximal information coefficient (MIC). A baseline valence and arousal classifier is designed based on Gaussian mixture models. Online training module is implemented by using AdaBoost. While the offline recognizer is trained on the acted data, the online testing data includes the speaker-independent data and the elicited data. Experimental results show that by introducing the online learning module our speech emotion recognition system can be better adapted to new data, which is an important character in real world applications.

scholarly journals Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5212 ◽  
Author(s):  
Tursunov Anvarjon ◽  
Mustaqeem ◽  
Soonil Kwon
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Recognition Performance ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Success Rates ◽  
Speech Database ◽  
High Recognition Accuracy ◽  
Signal Processing Methods ◽  
Frequency Features

Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition is a challenging task for a machine. In addition, making it smarter so that the emotions are efficiently recognized by AI is equally challenging. The speech signal is quite hard to examine using signal processing methods because it consists of different frequencies and features that vary according to emotions, such as anger, fear, sadness, happiness, boredom, disgust, and surprise. Even though different algorithms are being developed for the SER, the success rates are very low according to the languages, the emotions, and the databases. In this paper, we propose a new lightweight effective SER model that has a low computational complexity and a high recognition accuracy. The suggested method uses the convolutional neural network (CNN) approach to learn the deep frequency features by using a plain rectangular filter with a modified pooling strategy that have more discriminative power for the SER. The proposed CNN model was trained on the extracted frequency features from the speech data and was then tested to predict the emotions. The proposed SER model was evaluated over two benchmarks, which included the interactive emotional dyadic motion capture (IEMOCAP) and the berlin emotional speech database (EMO-DB) speech datasets, and it obtained 77.01% and 92.02% recognition results. The experimental results demonstrated that the proposed CNN-based SER system can achieve a better recognition performance than the state-of-the-art SER systems.

scholarly journals 3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 479 ◽  
Author(s):  
Noushin Hajarolasvadi ◽  
Hasan Demirel
Keyword(s):  
Emotion Recognition ◽  
Speech Signal ◽  
Expressed Emotion ◽  
Audio Signal ◽  
Research Direction ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Mel Frequency Cepstral Coefficients ◽  
Audio Features ◽  
3D Cnn

Detecting human intentions and emotions helps improve human–robot interactions. Emotion recognition has been a challenging research direction in the past decade. This paper proposes an emotion recognition system based on analysis of speech signals. Firstly, we split each speech signal into overlapping frames of the same length. Next, we extract an 88-dimensional vector of audio features including Mel Frequency Cepstral Coefficients (MFCC), pitch, and intensity for each of the respective frames. In parallel, the spectrogram of each frame is generated. In the final preprocessing step, by applying k-means clustering on the extracted features of all frames of each audio signal, we select k most discriminant frames, namely keyframes, to summarize the speech signal. Then, the sequence of the corresponding spectrograms of keyframes is encapsulated in a 3D tensor. These tensors are used to train and test a 3D Convolutional Neural network using a 10-fold cross-validation approach. The proposed 3D CNN has two convolutional layers and one fully connected layer. Experiments are conducted on the Surrey Audio-Visual Expressed Emotion (SAVEE), Ryerson Multimedia Laboratory (RML), and eNTERFACE’05 databases. The results are superior to the state-of-the-art methods reported in the literature.

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.

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.

Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition

2015 ◽  
Vol 6 (2) ◽  
pp. 57-68 ◽  
Author(s):  
Imen Trabelsi ◽  
Med Salim Bouhlel
Keyword(s):  
Emotion Recognition ◽  
Linear Prediction ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
Support Vector ◽  
Bhattacharyya Distance ◽  
Human Machine Interaction ◽  
Speech Database ◽  
Perceptual Linear Prediction

Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors, covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) kernel based on the Bhattacharyya distance is successfully used. CFSSubsetEval combined with Best first algorithm and Greedy stepwise were also utilized on the supervectors space in order to select the most important features. This framework is illustrated using Mel-frequency cepstral (MFCC) coefficients and Perceptual Linear Prediction (PLP) features on two different emotional databases namely the Surrey Audio-Expressed Emotion and the Berlin Emotional speech Database.

scholarly journals Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Pavol Partila ◽  
Miroslav Voznak ◽  
Jaromir Tovarek
Keyword(s):  
Emotion Recognition ◽  
Voice Quality ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Features Selection ◽  
Pattern Recognition Methods ◽  
Selection For ◽  
Combination Of Methods ◽  
The Impact

The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks,k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

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.

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