Machine Learning for Classification of Emotion in Speech

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%).

IRJET, 2021

Speech emotion recognition could even be a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In the lifetime of humans emotions play an important role in communication, the detection and analysis of an equivalent is of important importance in today's digital world of remote communication. Emotion detection may be a challenging task, because emotions are subjective. There is no common consensus on the way to measure or categorize them. We define a speech emotion recognition system as a set of methodologies that process and classify speech signals to detect emotions embedded in them. In this study we decide to detect underlying emotions in recorded speech by analyzing the acoustic features of the audio data of recordings. Emotion is an integral a part of human behavior and inherited property altogether mode of communication. We, human is well trained thought your experience reading recognition of varied emotions which make us more sensible and understandable. But just in case of machine, however, it can easily understand content based information like information in text, audio or video but still far behind to access the depth behind the content. There are three classes of features during a speech namely, the lexical features (the vocabulary used), the visual features (the expressions the speaker makes) and therefore the acoustic features (sound properties like pitch, tone, jitter, etc.).

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.

2016

Emotion recognition from Audio signal Recognition is a recent research topic in the Human Computer Interaction. The demand has risen for increasing communication interface between humans and digital media. Many researchers are working in order to improve their accuracy. But still there is lack of complete system which can recognize emotions from speech. In order to make the human and digital machine interaction more natural, the computer should able to recognize emotional states in the same way as human. The efficiency of emotion recognition system depends on type of features extracted and classifier used for detection of emotions. There are some fundamental emotions such as: Happy, Angry, Sad, Depressed, Bored, Anxiety, Fear and Nervous. These signals were preprocessed and analyzed using various techniques. In feature extraction various parameters used to form a feature vector are: fundamental frequency, pitch contour, formants, duration (pause length ratio) etc. These features are...

Social Media and Machine Learning, 2019

This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.

The human voice can be characterized by several attributes such as pitch, timbre, loudness, and vocal tone. It has often been observed that humans express their emotions by varying different vocal attributes during speech generation. This paper presents an algorithmic approach for detection of human emotions with the help speech .The prime objective of this paper is to recognize emotions in speech and classify them in 6 emotion output classes namely angry, fear, disgust, happy, sad and neutral. The proposed approach is based upon the Mel Frequency Cepstral coefficients (MFCC) uses Crema-D database of emotional speech. Data Augmention is perfomed on input data audio file, such as Noise, High Speed, Low Speed etc. are added, thus more the varied data is available to the model better the model understands. Feature extraction is done using MFCC and then the extracted features are Normalized(for Independent Variable), Label Encoding(for Dependent Variable(for SVM,RF)),One Hot Encoding(for Dependent Variable(for CNN)) is done. After this the dataset is divided into Train, Test and given to different models such as Convolutional Neural Network(CNN),Support Vector Machine(SVM), Random Forest(RF) for Emotion prediction. We report accuracy, f-score, precision and recall for the different experiment settings we evaluated our models in. Convolutional Neural Network(CNN) was found to have the highest accuracy and predicted correct emotion 88.21% of the time. Hence, deduction of human emotions through speech analysis has a practical plausibility and could potentially be beneficial for improving human conversational and persuasion skills.

Emotion recognition from speech is an important area in research that represents human-computer interaction. The main purpose of this paper is to present literature review of different features and techniques used for speech emotion recognition. The survey represents the importance of choosing different classification model and features for speech emotion recognition. Speech emotion recognition databases are also reviewed in this paper for the purpose of identifying the number of speakers, language used and emotion classification till date.

Speech Emotion Recognition is a field of artificial intelligence and machine learning which is used to recognize emotion from speech. Speech is language through which humans vocally communicate. Each language uses phonetic combinations of vowel and consonant sounds that form the sound of its words. An emotion expresses a human's mental state and is generated subconsciously. Developing machines that understand, recognize emotion from speech will make human-machine interaction more clear and natural. In this paper, an intelligent model is proposed to recognize the emotion of the user based on their speech using a deep learning algorithm. Convolutional Neural Network (CNN) is the deep learning algorithm used along with feature extraction techniques to recognize emotion from speech.

2008

The recognition of the emotional state of the speaker is a research area that has received great interest in the last years. The main goal is to improve voiced-based human-machine interactions. Most of the recent research on this domain has focused the studies in the prosodic features and the speech signal spectrum characteristics. However, there are many other characteristics and techniques which have not been explored in emotion recognition systems. In this work, a study of the performance of Gaussian mixtures models and hidden Markov models is presented. For the hidden Markov models, several configurations have been used, including an analysis of the optimal number of states. Results show the influence of number of Gaussian components and states. The performance of the classifiers has been evaluated with 3 to 7 emotions in spontaneous emotional speech and with speaker independence. In the analysis of three emotions: neutral, sadness and anger, the recognition rate by the Gaussian mixture classifiers was 93% and with hidden Markov models it was 97%. In the recognition of seven emotions, the accuracy was 67% with the Gaussian mixtures models and 76% in the evaluation of hidden Markov models.

Wseas Transactions on Information Science and Applications, 2008

The typical method of estimation of emotion in speech has the 3 steps. First, researchers collect a lot of human speech. Next, researchers get speech features from human speech using frequency analysis and calculate the statistical value of them. Finally they make a classifier from the statistical value using a learning algorithm. Most researchers consider the collection of human speech, feature selection and learning algorithm to increase the validity of estimation. But the validity of estimation is not high. In this paper, we propose the 3 new methods to enhance the typical method of estimation of emotion in speech. First method is that we use synthetic speech to make a classifier. Second method is that we use not only mean and maximum but also Standard Deviation (SD), skewness and kurtosis to make a classifier. Third method is that we use the classifier for each emotion. In order to evaluate our approach, we did experiments. Experimental results show the possibility in which our approach is effective for improving the former method.

Recent Trends in Intensive Computing, 2021

Speech emotion detection has been extremely relevant in today’s digital culture in recent years. RAVDESS, TESS, and SAVEE Datasets were used to train the model in our project. To determine the precision of each algorithm with each dataset, we looked at ten separate Machine Learning Algorithms. Following that, we cleaned the datasets by using the mask feature to eliminate unnecessary background noise, and then we applied all 10 algorithms to this clean speech dataset to improve accuracy. Then we look at the accuracies of all ten algorithms and see which one is the greatest. Finally, by using the algorithm, we could calculate the number of sound files correlated with each of the emotions described in those datasets.

International Journal of Advanced Trends in Computer Science and Engineering, 2021

As individuals discourse is among the most regular approach to communicate. We depend huge amount on it that we perceive its significance when falling back on other correspondence structures like messages and instant messages where we frequently use emoticons to communicate the feelings related with the messages. As feelings assume an essential part in correspondence, the identification and investigation of the equivalent is of crucial significance in the present computerized universe of distant correspondence. Feeling recognition is a difficult undertaking, since feelings are abstract. There is no normal agreement on the best way to quantify or sort them. We characterize a SER framework as an assortment of procedures that cycle and order discourse signs to distinguish feelings installed in them. Such a framework can discover use in a wide assortment of use zones like intuitive voice based-partner or guest specialist discussion investigation. In this examination we endeavor to distinguish fundamental feelings in recorded discourse by breaking down the acoustic highlights of the sound information of accounts.

Emotions play a very important role in human-human and human-machine communication. They can be expressed by voice, bodily gestures, and facial movements. People's acceptance of any kind of intelligent device depends, to a large extent, on how the device reflects emotions. This is the reason why automatic emotion recognition is a recent research topic. In this paper we deal with automatic emotion recognition from human voice. Numerous papers in this field deal with database creation and with the examination of acoustic features appropriate for such recognition, but only few attempts were made to compare different emotional segmentation units that are needed to recognize the emotions in spontaneous speech properly. In the Laboratory of Speech Acoustics experiments were ran to examine the effect of diverse speech segment lengths on recognition performance. An emotional database was prepared on the basis of three different segmentation levels: word, intonational phrase and sentence. Automatic recognition tests were conducted using Support Vector Machines with four basic emotions: neutral, anger, sad, and joy. The analysis of the results clearly shows that intonation phrase-sized speech units give the best performance in emotional recognition in continuous speech.

International Journal for Research in Applied Science and Engineering Technology Ijraset, 2020

This paper is an effort at developing a Speech Emotion Identifier model by implementing Librosa and sklearn libraries on a RAVDESS dataset. It gives the reader an insight on the way to detect a human’s emotion based on the speech, taken as input audio file. A newly developed speech signal model is applied to provide the user with the likelihood that the given speech is a response to a given emotion. This particular model is built using convolution neural networks (CNN) and classifiers namely Decission Tree, Random Forest and Multi-Layer Perceptron This model finds its applications in various real-world scenarios and therefore the most potent example for the identical would be in Customer care services where the staffs keep changing their way of pitching by recognizing Customers’ emotion from their speech so as to improve their quality of services provided. This paper presents the feasibility of extraction of MFCC features within the model. This model takes into consideration three different classifiers MLP, Random Forest and Decision Tree and by taking a combination of these three, we get the best possible accuracy as output.

Speaker emotion recognition is achieved through processing methods that include isolation of the speech signal and extraction of selected features for the final classification. In terms of acoustics, speech processing techniques offer extremely valuable paralinguistic information derived mainly from prosodic and spectral features. In some cases, the process is assisted by speech recognition systems, which contribute to the classification using linguistic information. Both frameworks deal with a very challenging problem, as emotional states do not have clear-cut boundaries and often differ from person to person. In this article, research papers that investigate emotion recognition from audio channels are surveyed and classified, based mostly on extracted and selected features and their classification methodology. Important topics from different classification techniques, such as databases available for experimentation, appropriate feature extraction and selection methods, classifiers and performance issues are discussed, with emphasis on research published in the last decade. This survey also provides a discussion on open trends, along with directions for future research on this topic.