Speech Emotion Recognition using Support Vector Machine

International Journal Of …, 2011

This paper introduces a approach to emotion recognition from speech signal using SVM as a classifier. Speech Emotion Recognition (SER) is a current research area in the field of Human Computer Interaction (HCI) with wide range of applications. The speech features such as, Mel Frequency cepstrum coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC) are extracted from speech utterance. The Support Vector Machine(SVM) is used as classifier to classify different emotional states such as anger, happiness, sadness, neutral, fear. The Berlin emotion database and Hindi emotion database are used for extracting the features from emotional speech .wav files. The recognition rates by implemented SVM are 62% and 71.66% for Berlin database and Hindi database respectively. The recognition rates by LIBSVM using RBF for Berlin database are 99.39% for cost value c=8. The recognition rates by LIBSVM using RBF kernel function for Hindi database are 78.33%.The accuracy rates by LIBSVM using Linear RBF kernel function for German independent files is 68.902%.

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2018

Speech one of the biometric characteristic owned by human being, as well as fingerprint, DNA, retina of the eyes and so not the two human beings who have the same voice. Human emotion is a matter that can only be predicted through the face of a person, or from the change of facial expression but it turns out human emotions can also be detected through the spoken voice. Someone emotion are happy, angry, neutral, sad, and surprise can be detected through speech signal. The development of voice recognition system is still running at this moment. So that ini this research, the analysis of someone emotion through speech signal. Some related research about the sound aims to have process of identity recognition gender recognition, Emotion recognition based on conversation. In this research the writer does research on the emotional classification of speech two classes started from happy, angry, neutral, sad and surprise while the used algorithm in this research is SVM (Support Vector Machin...

2019

Speech is the most natural form of communication between human beings. In the field of Human Computer Interaction(HCI) speech emotion recognition system is used. Researchers have been trying to develop a system more like human , emotion recognizing robots is an example of it. By using the speech features emotions are recognized from speech signals. Various speech features are MFCC(Mel Frequency Cepstral Coefficient), pitch, energy, intensity, speaking rate, voice quality, etc. Speech has many parameters which have great weightage in recognizing emotion namely prosodic and acoustic features. SVM(Support Vector Machine) is used as a classifier, which is used to classify the different emotions like happy, angry, sad, fear, neutral, etc. With the help of multiclass SVM, obtain more than two emotions.

International Journal of Computer Applications

Creating an accurate Speech Emotion Recognition (SER) system depends on extracting features relevant to that of emotions from speech. In this paper, the features that are extracted from the speech samples include Mel Frequency Cepstral Coefficients (MFCC), energy, pitch, spectral flux, spectral roll-off and spectral stationarity. In order to avoid the 'curse of dimensionality', statistical parameters, i.e. mean, variance, median, maximum, minimum, and index of dispersion have been applied on the extracted features. For classifying the emotion in an unknown test sample, Support Vector Machines (SVM) has been chosen due to its proven efficiency. Through experimentation on the chosen features, an average classification accuracy of 86.6% has been achieved using one-v/s-all multi-class SVM which is further improved to 100% when reduced to binary form problem. Classifier metrics viz. precision, recall, and F-score values show that the proposed system gives improved accuracy for Em...

International Journal for Research in Applied Science and Engineering Technology, 2021

In the past decade a lot of research has gone into Automatic Speech Emotion Recognition (SER). The primary objective of SER is to improve man-machine interface. It can also be used to monitor the psycho physiological state of a person in lie detectors. In recent time, speech emotion recognition also finds its applications in medicine and forensics. In this paper 7 emotions are recognized using pitch and prosody features. Majority of the speech features used in this work are in time domain. Support Vector Machine (SVM) classifier has been used for classifying the emotions. Berlin emotional database is chosen for the task. A good recognition rate of 81% was obtained. The paper that was considered as the reference for our work recognized 4 emotions and obtained a recognition rate of 94.2%. The reference paper also used hybrid classifier thus increasing complexity but can only recognize 4 emotions.

The article presents an analysis of the possibility of recognizing from speech signal in Polish language. In order to perform experiments a database containing speech recordings with emotional content was created. On its basis, extraction of features from the speech signals was performed. The most important step was to determine which of the previously extracted features were the most suitable to distinguish emotions and with what accuracy the emotions could be classified. Two feature selection methods-Sequential Forward Search (SFS) and t-statistics were examined. Emotion classification was implemented using k-Nearest Neighbor (k-NN), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classifiers. Classification was carried out for pairs of emotions. The best results were obtained for classifying neutral and fear (91.9%) and neutral and joy emotions (89.6%).

2016

This paper presents multi-class speech emotion recognition developed using Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) super vectors. Input to the system is in the form of speech utterances. Seven emotions (Happiness, Sadness, Anger, Fear, Surprise, Disgust, and Neutral) were considered in this study. For each of these emotions, feature extraction and normalisation were implemented using Praat Scripting Language (PSL) to compute for the average pitch and intensity and to perform batch processing of the training data. The processed features were then subjected to SVM-GMM for modelling and classification. A data set comprising of 175 speech utterances collected from selected individuals were divided into 140 training and 35 test data. To measure the performance of the developed SVM-GMM classifier, performance measures such as precision, recall, F score, accuracy and error rate were calculated. Two levels of averages were computed: microand macroaveraging. The initi...

International Journal of Scientific Research in Science and Technology, 2022

Emotion is a natural feeling which is distinguished from reasoning or knowledge, it is a strong feeling derived from one’s circumstance or surroundings. With the increase in man to machine interaction, speech analysis has become an integral part in reducing the gap between physical and digital world. An important sub field within this domain is the recognition of emotion in speech signals, which was traditionally studied in linguistics and psychology. Speech emotion recognition is a field having diverse applications. When implemented the Speech Emotion Recognition (SER) will be able to understand different human emotion such as anger, fear, happiness, sadness etc. Speech is a medium of expression of one’s perspective or feelings to other. Emotion recognition from audio signal requires feature extraction and classifier training. The feature vector consists of elements of the audio signal which characterize speaker specific features such as tone, pitch, energy, which is crucial to train the classifier model to recognize a particular emotion accurately. Thus, with the help of SER we can make conversations between human and computer more realistic and natural. Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with wide range of applications. The speech features such as, Mel Frequency cepstrum coefficients (MFCC) and Mel Energy Spectrum Dynamic Coefficients (MEDC) are extracted from speech utterance. The Support Vector Machine (SVM) is used as classifier to classify different emotional states such as anger, happiness, sadness, neutral, fear, from Berlin emotional database.

This paper presents a machine learning approach to automatic recognition of human emotions from speech. The approach consists of three steps. First, numerical features are extracted from the sound database by using audio feature extractor. Then, feature selection method is used to select the most relevant features. Finally, a machine learning model is trained to recognize seven universal emotions: anger, fear, sadness, happiness, boredom, disgust and neutral. A thorough ML experimental analysis is performed for each step. The results showed that 300 (out of 1582) features, as ranked by the gain ratio, are sufficient for achieving 86% accuracy when evaluated with 10 fold cross-validation. SVM achieved the highest accuracy when compared to KNN and Naive Bayes. We additionally compared the accuracy of the standard SVM (with default parameters) and the one enhanced by Auto-WEKA (optimized algorithm parameters) using the leave-one-speaker-out technique. The results showed that the SVM enhanced with Auto-WEKA achieved significantly better accuracy than the standard SVM, i.e., 73% and 77% respectively. Finally, the results achieved with the 10 fold cross-validation are comparable and similar to the ones achieved by a human, i.e., 86% accuracy in both cases. Even more, low energy emotions (boredom, sadness and disgust) are better recognized by our machine learning approach compared to the human. Povzetek: Prepoznavanje čustev iz govora s pomočjo strojnega učeanja

Perception in Multimodal Dialogue Systems, 2008

This study presents an approach for emotion classification of speech utterances based on ensemble of support vector machines. We considered feature level fusion of statistical values of the MFCC, total energy and F0 as input feature vectors, and choose bagging method to ensemble of SVM classifiers. Additionally, we also present a new emotional dataset based on a popular animation film, Finding Nemo. Speech utterances are directly extracted from video audio channel including all background noise. Then, totally 2054 utterances from 24 speakers were annotated by a group of volunteers based on seven emotion categories, and we selected 250 utterances each for training and test sets. Our approach has been tested on our newly developed dataset besides publically available datasets of DES and EmoDB. Experiments showed that our approach 77.5% and 66.8% overall accuracy for four and five class emotional speech classification on EFN dataset respectively. In addition, we achieved an overall accuracy of 67.6% on DES (five classes) and 63.5% on EmoDB (seven classes) dataset using ensemble of SVM's with 10 fold cross-validation.

that are input to the support vector machine (SVM), which will analyze them with the stored database recognize the emotion.

Speech emotions recognition has become one of the active research areas in speech processing and human computer interaction based applications. An experimental study is conducted in this paper with two spectral features (Rasta-PLP and MFCC), four different emotions (Angry, Happy, Neutral and Sad) and five different learning classifiers (K-star, Logit boost, J48, LAD Tree and Random forest). The focus of this work is to evaluate the performance of learning classifiers in term of classification accuracy and analyzing the effect of emotion and spectral features (MFCC and Rasta PLP) on spoken utterances in Urdu language with four different emotions. Demonstrative experiments were conducted in to two phases. Experimental results of the first phase shows that the combination of spectral features provides significant classification accuracy of learning classifiers as compared to the both features are considered individually. Whereas, Two-way ANOVA testing in the second phase of experiments show that both the factors, emotions and features as well as their interaction have significant effect on speech as the P value is less than the confidence interval.

Seventh IEEE International Symposium on Multimedia (ISM'05), 2005

This paper discusses the use of a combination of support vector machine and decision tree learning for recognizing four emotions in speech, which are Neutral, Angry, Lombard, and Loud. The base features selected were pitch, derivative of pitch, energy, speaking rate, formants, bandwidths, and Mel Frequency Cepstral Coefficients. Three methods of combining learned support vector machine and decision tree classifiers were proposed, namely, minimum misclassification, maximum accuracy, and dominant class. Using the Speech Under Simulated and Actual Stress database, the average accuracy from the minimum misclassification, maximum accuracy, and dominant class methods were 72.4%, 70.8%, 71.3% respectively as opposed to 63.9% and 67.4% which were obtained by using support vector machine and decision tree learning alone.

Speech Emotion Recognition is a recent research topic in the Human Computer Interaction (HCI) field. The need has risen for a more natural communication interface between humans and computer, as computers have become an integral part of our lives. A lot of work currently going on to improve the interaction between humans and computers. To achieve this goal, a computer would have to be able to distinguish its present situation and respond differently depending on that observation. Part of this process involves understanding a user"s emotional state. To make the human computer interaction more natural, the objective is that computer should be able to recognize emotional states in the same as human does. The efficiency of emotion recognition system depends on type of features extracted and classifier used for detection of emotions.

2013

Human- Computer intelligent interaction (HCII) is an emerging field of science aimed at providing natural ways for humans to use computer as aids. Machine intelligence needs to include emotional intelligence it is argued that for the computer to be able to interact with humans, it needs to have the communication skills of human. One of these skills is the ability to understand the emotional state of the person. Two recognition methods namely K-Nearest Neighbor (K-NN) and Support vector machine (SVM) classifier have been experimented and compared. The paper explores the simplicity and effectiveness of SVM classifier for designing the real-time emotion recognition system.