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. The proposed system aims at identification of basic emotional states such as anger, joy, neutral and sadness from human speech. While classifying different emotions, features like MFCC (Mel Frequency Cepstral Coefficient) and Energy is used. In this paper, Standard Emotional Database i.e. English Database is used which gives the satisfactory detection of emotions than recorded samples of emotions. This methodology describes and compares the performances of Learning Vector Quantization Neural Network (LVQ NN), Multiclass Support Vector Machine (SVM) and their combination for emotion recognition.
IRJET- Emotion recognition using Speech Signal: A ReviewIRJET Journal
This document provides a review of speech emotion recognition techniques. It discusses how speech emotion recognition systems work, including common features extracted from speech like MFCCs and LPC coefficients. Classification techniques used in these systems are also examined, such as DTW, ANN, GMM, and K-NN. The document concludes that speech emotion recognition could be useful for applications requiring natural human-computer interaction, like car systems that monitor driver emotion or educational tutorials that adapt based on student emotion.
This paper introduces new features based on histograms of MFCC extracted from audio files to improve emotion recognition from speech. Experimental results on the Berlin and PAU databases using SVM and Random Forest classifiers show the proposed features achieve better classification results than current methods. Detailed analysis is provided on speech type (acted vs natural) and gender.
On the use of voice activity detection in speech emotion recognitionjournalBEEI
Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
This document proposes an automatic emotion recognition system that analyzes audio information to classify human emotions. It uses spectral features and MFCC coefficients for feature extraction from voice signals. Then, a deep learning-based LSTM algorithm is used for classification. The system is evaluated on three audio datasets. Recurrent convolutional neural networks are proposed to capture temporal and frequency dependencies in speech spectrograms. The system aims to improve on existing methods which have lower accuracy and require more computational resources for implementation.
Novel Methodologies for Classifying Gender and Emotions Using Machine Learnin...BRIGHT WORLD INNOVATIONS
This document proposes a system for classifying gender and emotions from human voice using machine learning algorithms. It involves preprocessing voice data, removing noise using hidden Markov models, extracting features using discrete wavelet transforms, and classifying gender and emotion using k-nearest neighbors. The system combines gender and emotion classification into a single system, achieving higher accuracy than systems that only classify one or the other. Evaluation shows the proposed system achieves 97% accuracy, outperforming existing systems with accuracies of 75-86%.
Automatic speech emotion and speaker recognition based on hybrid gmm and ffbnnijcsa
In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion
of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker
influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for
experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in
training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech
database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise
and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a
significant impact on the accuracy of speaker recognition.
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition TechniqueCSCJournals
Automatic speaker recognition system is used to recognize an unknown speaker among several reference speakers by making use of speaker-specific information from their speech. In this paper, we introduce a novel, hierarchical, text-independent speaker recognition. Our baseline speaker recognition system accuracy, built using statistical modeling techniques, gives an accuracy of 81% on the standard MIT database and our baseline gender recognition system gives an accuracy of 93.795%. We then propose and implement a novel state-space pruning technique by performing gender recognition before speaker recognition so as to improve the accuracy/timeliness of our baseline speaker recognition system. Based on the experiments conducted on the MIT database, we demonstrate that our proposed system improves the accuracy over the baseline system by approximately 2%, while reducing the computational time by more than 30%.
This document summarizes Dongang Wang's speech emotion recognition project which compares feature selection and classification methods. Wang selects mel-frequency cepstral coefficients (MFCCs) and energy as features. For methods, Wang tests Gaussian mixture models (GMMs), discrete hidden Markov models (HMMs), and continuous HMMs including Kalman filters. Testing on German and English corpora, continuous HMMs achieved the best average accuracy of 61.67%, outperforming GMMs and discrete HMMs. While results are promising, Wang notes challenges in recognizing emotion across languages and speakers.
IRJET- Emotion recognition using Speech Signal: A ReviewIRJET Journal
This document provides a review of speech emotion recognition techniques. It discusses how speech emotion recognition systems work, including common features extracted from speech like MFCCs and LPC coefficients. Classification techniques used in these systems are also examined, such as DTW, ANN, GMM, and K-NN. The document concludes that speech emotion recognition could be useful for applications requiring natural human-computer interaction, like car systems that monitor driver emotion or educational tutorials that adapt based on student emotion.
This paper introduces new features based on histograms of MFCC extracted from audio files to improve emotion recognition from speech. Experimental results on the Berlin and PAU databases using SVM and Random Forest classifiers show the proposed features achieve better classification results than current methods. Detailed analysis is provided on speech type (acted vs natural) and gender.
On the use of voice activity detection in speech emotion recognitionjournalBEEI
Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.
This document proposes an automatic emotion recognition system that analyzes audio information to classify human emotions. It uses spectral features and MFCC coefficients for feature extraction from voice signals. Then, a deep learning-based LSTM algorithm is used for classification. The system is evaluated on three audio datasets. Recurrent convolutional neural networks are proposed to capture temporal and frequency dependencies in speech spectrograms. The system aims to improve on existing methods which have lower accuracy and require more computational resources for implementation.
Novel Methodologies for Classifying Gender and Emotions Using Machine Learnin...BRIGHT WORLD INNOVATIONS
This document proposes a system for classifying gender and emotions from human voice using machine learning algorithms. It involves preprocessing voice data, removing noise using hidden Markov models, extracting features using discrete wavelet transforms, and classifying gender and emotion using k-nearest neighbors. The system combines gender and emotion classification into a single system, achieving higher accuracy than systems that only classify one or the other. Evaluation shows the proposed system achieves 97% accuracy, outperforming existing systems with accuracies of 75-86%.
Automatic speech emotion and speaker recognition based on hybrid gmm and ffbnnijcsa
In this paper we present text dependent speaker recognition with an enhancement of detecting the emotion
of the speaker prior using the hybrid FFBN and GMM methods. The emotional state of the speaker
influences recognition system. Mel-frequency Cepstral Coefficient (MFCC) feature set is used for
experimentation. To recognize the emotional state of a speaker Gaussian Mixture Model (GMM) is used in
training phase and in testing phase Feed Forward Back Propagation Neural Network (FFBNN). Speech
database consisting of 25 speakers recorded in five different emotional states: happy, angry, sad, surprise
and neutral is used for experimentation. The results reveal that the emotional state of the speaker shows a
significant impact on the accuracy of speaker recognition.
A Novel, Robust, Hierarchical, Text-Independent Speaker Recognition TechniqueCSCJournals
Automatic speaker recognition system is used to recognize an unknown speaker among several reference speakers by making use of speaker-specific information from their speech. In this paper, we introduce a novel, hierarchical, text-independent speaker recognition. Our baseline speaker recognition system accuracy, built using statistical modeling techniques, gives an accuracy of 81% on the standard MIT database and our baseline gender recognition system gives an accuracy of 93.795%. We then propose and implement a novel state-space pruning technique by performing gender recognition before speaker recognition so as to improve the accuracy/timeliness of our baseline speaker recognition system. Based on the experiments conducted on the MIT database, we demonstrate that our proposed system improves the accuracy over the baseline system by approximately 2%, while reducing the computational time by more than 30%.
This document summarizes Dongang Wang's speech emotion recognition project which compares feature selection and classification methods. Wang selects mel-frequency cepstral coefficients (MFCCs) and energy as features. For methods, Wang tests Gaussian mixture models (GMMs), discrete hidden Markov models (HMMs), and continuous HMMs including Kalman filters. Testing on German and English corpora, continuous HMMs achieved the best average accuracy of 61.67%, outperforming GMMs and discrete HMMs. While results are promising, Wang notes challenges in recognizing emotion across languages and speakers.
This document discusses using deep neural networks for speech enhancement by finding a mapping between noisy and clean speech signals. It aims to handle a wide range of noises by using a large training dataset with many noise/speech combinations. Techniques like global variance equalization and dropout are used to improve generalization. Experimental results show improvements over MMSE techniques, with the ability to suppress nonstationary noise and avoid musical artifacts. The introduction provides background on speech enhancement, recognition using HMMs and other models, and the role of deep learning advances.
We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
This paper contains a report on an Audio-Visual Client Recognition System using Matlab software which identifies five clients and can be improved to identify as many clients as possible depending on the number of clients it is trained to identify which was successfully implemented. The implementation was accomplished first by visual recognition system implemented using The Principal Component Analysis, Linear Discriminant Analysis and Nearest Neighbour Classifier. A successful implementation of second part was achieved by audio recognition using Mel-Frequency Cepstrum Coefficient, Linear Discriminant Analysis and Nearest Neighbour Classifier the system was tested using images and sounds that have not been trained to the system to see whether it can detect an intruder which lead us to a very successful result with précised response to intruder.
Audio/Speech Signal Analysis for Depressionijsrd.com
The word “depressed†is a common everyday word. People might say "I am depressed" when in fact they mean "I am fed up because I have had a row, or failed an exam, or lost my job", etc. These ups and downs of life are common and normal. Most people recover quite quickly. Depression is identified by different methods. Here we are identified depression by MFCC (Mel Frequency Ceptral Coefficient) method. There are different parameters used for the identification of depressed speech and normal speech, but MFCCs based parameter is the most applicable information then other parameter because depressive speech or audio signal can contain more information in the higher energy bands when compared with normal speech.
This document outlines a research project for a Master's degree in computer engineering. The project aims to improve the performance of speaker verification systems in noisy environments. It will use voice activity detection (VAD) methods to estimate noise models and parallel model combination (PMC) to estimate noisy speech models. Two VAD methods will be implemented and compared: vector quantization VAD (VQVAD) and minimum mean squared error (MMSE). Experiments will be conducted on the NIST 1998 and NOISEX-92 databases at various signal-to-noise ratios to evaluate mel frequency cepstral coefficients using PMC. The results will be used to analyze the effectiveness of the VAD methods in improving speaker verification accuracy in noisy conditions.
IRJET- Study of Effect of PCA on Speech Emotion RecognitionIRJET Journal
This document discusses speech emotion recognition using principal component analysis (PCA). It analyzes speech features like mel frequency cepstral coefficients, pitch, energy, and formant frequency from the Berlin database containing emotions like anger, sadness, happiness, and fear. PCA is applied to reduce the feature dimension and decorrelate features. A support vector machine classifier is then used to classify emotions based on the PCA-processed features. Results show applying PCA improves the classification accuracy compared to without using PCA, with accuracy increasing from 68% to 64.5% on average.
ANALYSIS OF SPEECH UNDER STRESS USING LINEAR TECHNIQUES AND NON-LINEAR TECHNI...cscpconf
Analysis of speech for recognition of stress is important for identification of emotional state of
person. This can be done using ‘Linear Techniques’, which has different parameters like pitch,
vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction
of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of
features extraction. Analysis is done using TU-Berlin (Technical University of Berlin) German
database. Here emotion recognition is done for different emotions like neutral, happy, disgust,
sad, boredom and anger. Emotion recognition is used in lie detector, database access systems, and in military for recognition of soldiers’ emotion identification during the war.
This document summarizes a research paper on classifying speech using Mel frequency cepstrum coefficients (MFCC) and power spectrum analysis. The paper reviews different classifiers used for speech emotion recognition, including neural networks, Gaussian mixture models, and support vector machines. It proposes using MFCC and power spectrum features as inputs to an artificial neural network classifier to identify emotions in speech, such as anger, happiness, sadness, and neutral states. Testing is performed on emotional speech samples to evaluate the performance and limitations of the proposed speech emotion recognition system.
This paper describe the morphing concept in which we convert the voice of any person into pre -analyzed or pre-recorded voice of any animals.As the user generate a pre-established voice, his pitch, timbre, vibrato and articulation can be modified to resemble those of a pre-recorded and pre-analyzed voice of animal. This technique is based on SMS. Thus using this concept we can develop many funny application and we can used this type of application in mobile device, personal computer etc. for enjoying the sometime of period.
Comparison of Feature Extraction MFCC and LPC in Automatic Speech Recognition...TELKOMNIKA JOURNAL
Speech recognition can be defined as the process of converting voice signals into the ranks of the
word, by applying a specific algorithm that is implemented in a computer program. The research of speech
recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is
the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for
speech recognition in Indonesian language. This is important because the method can produce a high
accuracy for a particular language does not necessarily produce the same accuracy for other languages,
considering every language has different characteristics. Thus this research hopefully can help further
accelerate the use of automatic speech recognition for Indonesian language. There are two main
processes in speech recognition, feature extraction and recognition. The method used for comparison
feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden
Markov Model (HMM). The test results showed that the MFCC method is better than LPC in Indonesian
language speech recognition.
This document discusses speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). It describes the process of feature extraction using MFCC which involves framing the speech signal, taking the Fourier transform of each frame, warping the frequencies using the mel scale, taking the logs of the powers at each mel frequency, and converting to cepstral coefficients. It then discusses feature matching techniques like vector quantization which clusters reference speaker features to create codebooks for comparison to unknown speakers. The document provides references for further reading on speech and speaker recognition techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Mfcc based enlargement of the training set for emotion recognition in speechsipij
Emotional state recognition through speech is being a very interesting research topic nowadays. Using
subliminal information of speech, denominated as “prosody”, it is possible to recognize the emotional state
of the person. One of the main problems in the design of automatic emotion recognition systems is the small
number of available patterns. This fact makes the learning process more difficult, due to the generalization
problems that arise under these conditions.
In this work we propose a solution to this problem consisting in enlarging the training set through the
creation the new virtual patterns. In the case of emotional speech, most of the emotional information is
included in speed and pitch variations. So, a change in the average pitch that does not modify neither the
speed nor the pitch variations does not affect the expressed emotion. Thus, we use this prior information in
order to create new patterns applying a gender dependent pitch shift modification in the feature extraction
process of the classification system. For this purpose, we propose a frequency scaling modification of the
Mel Frequency Cepstral Coefficients, used to classify the emotion. For this purpose, we propose a gender
dependent frequency scaling modification. This proposed process allows us to synthetically increase the
number of available patterns in the training set, thus increasing the generalization capability of the system
and reducing the test error. Results carried out with two different classifiers with different degree of
generalization capability demonstrate the suitability of the proposal.
CURVELET BASED SPEECH RECOGNITION SYSTEM IN NOISY ENVIRONMENT: A STATISTICAL ...ijcsit
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of
speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech
recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
Emotion Recognition based on audio signal using GFCC Extraction and BPNN Clas...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
This document summarizes research on speaker recognition technologies. It discusses how speaker recognition can be used for biometric authentication by analyzing a person's voiceprint. It reviews literature on MFCC-GMM models for text-independent speaker verification and the use of speaker recognition in biometric security systems. The document also outlines the basic components of a speaker recognition system, including enrollment, feature extraction using MFCCs, and verification through comparison to stored voice templates using algorithms like GMM.
Emotion Detection from Voice Based Classified Frame-Energy Signal Using K-Mea...ijseajournal
Emotion detection is a new research era in health informatics and forensic technology. Besides having some challenges, voice based emotion recognition is getting popular, as the situation where the facial image is not available, the voice is the only way to detect the emotional or psychiatric condition of a
person. However, the voice signal is so dynamic even in a short-time frame so that, a voice of the same person can differ within a very subtle period of time. Therefore, in this research basically two key criterion have been considered; firstly, this is clear that there is a necessity to partition the training data according
to the emotional stage of each individual speaker. Secondly, rather than using the entire voice signal, short time significant frames can be used, which would be enough to identify the emotional condition of the speaker. In this research, Cepstral Coefficient (CC) has been used as voice feature and a fixed valued kmeans clustered method has been used for feature classification. The value of k will depend on the number
of emotional situations in human physiology is being an evaluation. Consequently, the value of k does not necessarily consider the volume of experimental dataset. In this experiment, three emotional conditions: happy, angry and sad have been detected from eight female and seven male voice signals. This methodology has increased the emotion detection accuracy rate significantly comparing to some recent works and also reduced the CPU time of cluster formation and matching.
This document discusses speech-based emotion recognition using Gaussian mixture models (GMM). GMMs are statistical models that are well-suited for developing emotion recognition systems from large feature datasets. The document proposes using GMMs trained on excitation features extracted from speech signals to classify emotions into categories like happy, angry, sad, and neutral. It describes extracting excitation source features through linear predictive coding analysis to capture information about a speaker's vocal excitation source. The goal is to develop a GMM-based emotion recognition system that can classify emotions in conversations.
This document presents a text-dependent speaker recognition system using neural networks that aims to improve recognition accuracy. It proposes changing the number of Mel Frequency Cepstral Coefficients (MFCCs) used in training. Voice Activity Detection is also used as a preprocessing step. Experimental results show recognition accuracy increases from 70.41% to a maximum of 92.91% as the number of MFCCs increases from 14 to 20, but then decreases with more MFCCs. The system is implemented on a Raspberry Pi for hardware acceleration.
This document presents research on emotion recognition from speech using a combination of MFCC and LPCC features with support vector machine (SVM) classification. Two databases were used: the Berlin Emotional Database and SAVEE database. MFCC and LPCC features were extracted from the speech samples and combined. SVM with radial basis function kernel achieved the highest accuracy of 88.59% for emotion recognition on the Berlin database using the combined features. Confusion matrices are presented to evaluate performance on each database.
This document discusses using deep neural networks for speech enhancement by finding a mapping between noisy and clean speech signals. It aims to handle a wide range of noises by using a large training dataset with many noise/speech combinations. Techniques like global variance equalization and dropout are used to improve generalization. Experimental results show improvements over MMSE techniques, with the ability to suppress nonstationary noise and avoid musical artifacts. The introduction provides background on speech enhancement, recognition using HMMs and other models, and the role of deep learning advances.
We propose a model for carrying out deep learning based multimodal sentiment analysis. The MOUD dataset is taken for experimentation purposes. We developed two parallel text based and audio basedmodels and further, fused these heterogeneous feature maps taken from intermediate layers to complete thearchitecture. Performance measures–Accuracy, precision, recall and F1-score–are observed to outperformthe existing models.
This paper contains a report on an Audio-Visual Client Recognition System using Matlab software which identifies five clients and can be improved to identify as many clients as possible depending on the number of clients it is trained to identify which was successfully implemented. The implementation was accomplished first by visual recognition system implemented using The Principal Component Analysis, Linear Discriminant Analysis and Nearest Neighbour Classifier. A successful implementation of second part was achieved by audio recognition using Mel-Frequency Cepstrum Coefficient, Linear Discriminant Analysis and Nearest Neighbour Classifier the system was tested using images and sounds that have not been trained to the system to see whether it can detect an intruder which lead us to a very successful result with précised response to intruder.
Audio/Speech Signal Analysis for Depressionijsrd.com
The word “depressed†is a common everyday word. People might say "I am depressed" when in fact they mean "I am fed up because I have had a row, or failed an exam, or lost my job", etc. These ups and downs of life are common and normal. Most people recover quite quickly. Depression is identified by different methods. Here we are identified depression by MFCC (Mel Frequency Ceptral Coefficient) method. There are different parameters used for the identification of depressed speech and normal speech, but MFCCs based parameter is the most applicable information then other parameter because depressive speech or audio signal can contain more information in the higher energy bands when compared with normal speech.
This document outlines a research project for a Master's degree in computer engineering. The project aims to improve the performance of speaker verification systems in noisy environments. It will use voice activity detection (VAD) methods to estimate noise models and parallel model combination (PMC) to estimate noisy speech models. Two VAD methods will be implemented and compared: vector quantization VAD (VQVAD) and minimum mean squared error (MMSE). Experiments will be conducted on the NIST 1998 and NOISEX-92 databases at various signal-to-noise ratios to evaluate mel frequency cepstral coefficients using PMC. The results will be used to analyze the effectiveness of the VAD methods in improving speaker verification accuracy in noisy conditions.
IRJET- Study of Effect of PCA on Speech Emotion RecognitionIRJET Journal
This document discusses speech emotion recognition using principal component analysis (PCA). It analyzes speech features like mel frequency cepstral coefficients, pitch, energy, and formant frequency from the Berlin database containing emotions like anger, sadness, happiness, and fear. PCA is applied to reduce the feature dimension and decorrelate features. A support vector machine classifier is then used to classify emotions based on the PCA-processed features. Results show applying PCA improves the classification accuracy compared to without using PCA, with accuracy increasing from 68% to 64.5% on average.
ANALYSIS OF SPEECH UNDER STRESS USING LINEAR TECHNIQUES AND NON-LINEAR TECHNI...cscpconf
Analysis of speech for recognition of stress is important for identification of emotional state of
person. This can be done using ‘Linear Techniques’, which has different parameters like pitch,
vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction
of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of
features extraction. Analysis is done using TU-Berlin (Technical University of Berlin) German
database. Here emotion recognition is done for different emotions like neutral, happy, disgust,
sad, boredom and anger. Emotion recognition is used in lie detector, database access systems, and in military for recognition of soldiers’ emotion identification during the war.
This document summarizes a research paper on classifying speech using Mel frequency cepstrum coefficients (MFCC) and power spectrum analysis. The paper reviews different classifiers used for speech emotion recognition, including neural networks, Gaussian mixture models, and support vector machines. It proposes using MFCC and power spectrum features as inputs to an artificial neural network classifier to identify emotions in speech, such as anger, happiness, sadness, and neutral states. Testing is performed on emotional speech samples to evaluate the performance and limitations of the proposed speech emotion recognition system.
This paper describe the morphing concept in which we convert the voice of any person into pre -analyzed or pre-recorded voice of any animals.As the user generate a pre-established voice, his pitch, timbre, vibrato and articulation can be modified to resemble those of a pre-recorded and pre-analyzed voice of animal. This technique is based on SMS. Thus using this concept we can develop many funny application and we can used this type of application in mobile device, personal computer etc. for enjoying the sometime of period.
Comparison of Feature Extraction MFCC and LPC in Automatic Speech Recognition...TELKOMNIKA JOURNAL
Speech recognition can be defined as the process of converting voice signals into the ranks of the
word, by applying a specific algorithm that is implemented in a computer program. The research of speech
recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is
the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for
speech recognition in Indonesian language. This is important because the method can produce a high
accuracy for a particular language does not necessarily produce the same accuracy for other languages,
considering every language has different characteristics. Thus this research hopefully can help further
accelerate the use of automatic speech recognition for Indonesian language. There are two main
processes in speech recognition, feature extraction and recognition. The method used for comparison
feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden
Markov Model (HMM). The test results showed that the MFCC method is better than LPC in Indonesian
language speech recognition.
This document discusses speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). It describes the process of feature extraction using MFCC which involves framing the speech signal, taking the Fourier transform of each frame, warping the frequencies using the mel scale, taking the logs of the powers at each mel frequency, and converting to cepstral coefficients. It then discusses feature matching techniques like vector quantization which clusters reference speaker features to create codebooks for comparison to unknown speakers. The document provides references for further reading on speech and speaker recognition techniques.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Mfcc based enlargement of the training set for emotion recognition in speechsipij
Emotional state recognition through speech is being a very interesting research topic nowadays. Using
subliminal information of speech, denominated as “prosody”, it is possible to recognize the emotional state
of the person. One of the main problems in the design of automatic emotion recognition systems is the small
number of available patterns. This fact makes the learning process more difficult, due to the generalization
problems that arise under these conditions.
In this work we propose a solution to this problem consisting in enlarging the training set through the
creation the new virtual patterns. In the case of emotional speech, most of the emotional information is
included in speed and pitch variations. So, a change in the average pitch that does not modify neither the
speed nor the pitch variations does not affect the expressed emotion. Thus, we use this prior information in
order to create new patterns applying a gender dependent pitch shift modification in the feature extraction
process of the classification system. For this purpose, we propose a frequency scaling modification of the
Mel Frequency Cepstral Coefficients, used to classify the emotion. For this purpose, we propose a gender
dependent frequency scaling modification. This proposed process allows us to synthetically increase the
number of available patterns in the training set, thus increasing the generalization capability of the system
and reducing the test error. Results carried out with two different classifiers with different degree of
generalization capability demonstrate the suitability of the proposal.
CURVELET BASED SPEECH RECOGNITION SYSTEM IN NOISY ENVIRONMENT: A STATISTICAL ...ijcsit
Speech processing is considered as crucial and an intensive field of research in the growth of robust and efficient speech recognition system. But the accuracy for speech recognition still focuses for variation of context, speaker’s variability, and environment conditions. In this paper, we stated curvelet based Feature Extraction (CFE) method for speech recognition in noisy environment and the input speech signal is decomposed into different frequency channels using the characteristics of curvelet transform for reduce the computational complication and the feature vector size successfully and they have better accuracy, varying window size because of which they are suitable for non –stationary signals. For better word classification and recognition, discrete hidden markov model can be used and as they consider time distribution of
speech signals. The HMM classification method attained the maximum accuracy in term of identification rate for informal with 80.1%, scientific phrases with 86%, and control with 63.8 % detection rates. The objective of this study is to characterize the feature extraction methods and classification phage in speech
recognition system. The various approaches available for developing speech recognition system are compared along with their merits and demerits. The statistical results shows that signal recognition accuracy will be increased by using discrete Curvelet transforms over conventional methods.
Emotion Recognition based on audio signal using GFCC Extraction and BPNN Clas...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
In the present-day communications speech signals get contaminated due to
various sorts of noises that degrade the speech quality and adversely impacts
speech recognition performance. To overcome these issues, a novel approach
for speech enhancement using Modified Wiener filtering is developed and
power spectrum computation is applied for degraded signal to obtain the
noise characteristics from a noisy spectrum. In next phase, MMSE technique
is applied where Gaussian distribution of each signal i.e. original and noisy
signal is analyzed. The Gaussian distribution provides spectrum estimation
and spectral coefficient parameters which can be used for probabilistic model
formulation. Moreover, a-priori-SNR computation is also incorporated for
coefficient updation and noise presence estimation which operates similar to
the conventional VAD. However, conventional VAD scheme is based on the
hard threshold which is not capable to derive satisfactory performance and a
soft-decision based threshold is developed for improving the performance of
speech enhancement. An extensive simulation study is carried out using
MATLAB simulation tool on NOIZEUS speech database and a comparative
study is presented where proposed approach is proved better in comparison
with existing technique.
This document summarizes research on speaker recognition technologies. It discusses how speaker recognition can be used for biometric authentication by analyzing a person's voiceprint. It reviews literature on MFCC-GMM models for text-independent speaker verification and the use of speaker recognition in biometric security systems. The document also outlines the basic components of a speaker recognition system, including enrollment, feature extraction using MFCCs, and verification through comparison to stored voice templates using algorithms like GMM.
Emotion Detection from Voice Based Classified Frame-Energy Signal Using K-Mea...ijseajournal
Emotion detection is a new research era in health informatics and forensic technology. Besides having some challenges, voice based emotion recognition is getting popular, as the situation where the facial image is not available, the voice is the only way to detect the emotional or psychiatric condition of a
person. However, the voice signal is so dynamic even in a short-time frame so that, a voice of the same person can differ within a very subtle period of time. Therefore, in this research basically two key criterion have been considered; firstly, this is clear that there is a necessity to partition the training data according
to the emotional stage of each individual speaker. Secondly, rather than using the entire voice signal, short time significant frames can be used, which would be enough to identify the emotional condition of the speaker. In this research, Cepstral Coefficient (CC) has been used as voice feature and a fixed valued kmeans clustered method has been used for feature classification. The value of k will depend on the number
of emotional situations in human physiology is being an evaluation. Consequently, the value of k does not necessarily consider the volume of experimental dataset. In this experiment, three emotional conditions: happy, angry and sad have been detected from eight female and seven male voice signals. This methodology has increased the emotion detection accuracy rate significantly comparing to some recent works and also reduced the CPU time of cluster formation and matching.
This document discusses speech-based emotion recognition using Gaussian mixture models (GMM). GMMs are statistical models that are well-suited for developing emotion recognition systems from large feature datasets. The document proposes using GMMs trained on excitation features extracted from speech signals to classify emotions into categories like happy, angry, sad, and neutral. It describes extracting excitation source features through linear predictive coding analysis to capture information about a speaker's vocal excitation source. The goal is to develop a GMM-based emotion recognition system that can classify emotions in conversations.
This document presents a text-dependent speaker recognition system using neural networks that aims to improve recognition accuracy. It proposes changing the number of Mel Frequency Cepstral Coefficients (MFCCs) used in training. Voice Activity Detection is also used as a preprocessing step. Experimental results show recognition accuracy increases from 70.41% to a maximum of 92.91% as the number of MFCCs increases from 14 to 20, but then decreases with more MFCCs. The system is implemented on a Raspberry Pi for hardware acceleration.
This document presents research on emotion recognition from speech using a combination of MFCC and LPCC features with support vector machine (SVM) classification. Two databases were used: the Berlin Emotional Database and SAVEE database. MFCC and LPCC features were extracted from the speech samples and combined. SVM with radial basis function kernel achieved the highest accuracy of 88.59% for emotion recognition on the Berlin database using the combined features. Confusion matrices are presented to evaluate performance on each database.
This document presents research on emotion recognition from speech using a combination of MFCC and LPCC features with support vector machine (SVM) classification. Two databases were used: the Berlin Emotional Database and SAVEE database. MFCC and LPCC features were extracted from the speech samples and combined. SVM with radial basis function kernel achieved the highest accuracy of 88.59% for emotion recognition on the Berlin database using the combined features. Confusion matrices are presented to evaluate performance on each database.
This document discusses an approach to extract features from speech signals using Mel Frequency Cepstral Coefficients (MFCC). MFCC is a popular feature extraction technique that models human auditory perception. It involves preprocessing the speech signal, framing it, applying the discrete cosine transform to the mel scale filter bank energies, and optionally adding delta and acceleration features. MFCC extraction was tested on isolated word samples to obtain coefficients representing the short-term power spectrum. These coefficients can then be used for applications like speech recognition by matching patterns in the feature space.
Speech emotion recognition with light gradient boosting decision trees machineIJECEIAES
Speech emotion recognition aims to identify the emotion expressed in the speech by analyzing the audio signals. In this work, data augmentation is first performed on the audio samples to increase the number of samples for better model learning. The audio samples are comprehensively encoded as the frequency and temporal domain features. In the classification, a light gradient boosting machine is leveraged. The hyperparameter tuning of the light gradient boosting machine is performed to determine the optimal hyperparameter settings. As the speech emotion recognition datasets are imbalanced, the class weights are regulated to be inversely proportional to the sample distribution where minority classes are assigned higher class weights. The experimental results demonstrate that the proposed method outshines the state-of-the-art methods with 84.91% accuracy on the Berlin database of emotional speech (emo-DB) dataset, 67.72% on the Ryerson audio-visual database of emotional speech and song (RAVDESS) dataset, and 62.94% on the interactive emotional dyadic motion capture (IEMOCAP) dataset.
This document summarizes a research paper on speaker recognition using vocal tract features. It discusses extracting pitch using FFT as a vocal source feature and Mel Frequency Cepstral Coefficients (MFCCs) as vocal tract features. These features are used as inputs to a Support Vector Machine (SVM) classifier for binary speaker classification. Experimental results show the SVM achieves up to 94.42% accuracy in classifying speakers based on their MFCC features, and accuracy increases with higher signal-to-noise ratios when noise is added to speech signals. The paper concludes vocal source and tract features can be used together with SVM for text-independent speaker recognition.
A Review On Speech Feature Techniques And Classification TechniquesNicole Heredia
This document discusses speech feature extraction and classification techniques for speech recognition systems. It provides an overview of common feature extraction methods like MFCC and LPC, and classification algorithms like ANN and SVM. MFCC mimics human auditory perception but provides weak power spectrum, while LPC is easy to calculate but does not capture information at a linear scale. ANN can learn from data but is complex for large datasets, while SVM is accurate and suitable for pattern recognition but requires fixed-length coefficients. The document evaluates these techniques and concludes that MFCC performance is more efficient than LPC for feature extraction in speech recognition.
Speech Recognized Automation System Using Speaker Identification through Wire...IOSR Journals
This document describes a speech recognized automation system using speaker identification through wireless communication. The system uses a speech processor and MATLAB coding with MFCC algorithms to perform speech recognition and speaker identification. It then wirelessly controls electrical devices based on speech commands. Testing showed 80-85% accuracy for the actual speaker and lower (10-20%) accuracy for other speakers. Future work could involve improving speaker recognition accuracy as the number of speakers increases.
Speech Recognized Automation System Using Speaker Identification through Wire...IOSR Journals
This paper discusses the methodology for a project named “Speech Recognized Automation System
using Speaker Identification through wireless communication”. This project gives the design of Automation
system using wireless communication and speaker recognition using Matlab code. Straightforward
programming interface of Matlab makes it an ideal tool for speech analysis in project. This automation system
is useful for home appliances as well as in industry. This paper discusses the overall design of a wireless
automation system which is built and implemented. The speech recognition centers on recognition of speech
commands stored in data base of Matlab and it is matched with incoming voice command of speaker. Mel
Frequency Cepstral Coefficient (MFCC) algorithm is used to recognize the speech of speaker and to extract
features of speech. It uses low-power RF ZigBee transceiver wireless communication modules which are
relatively cheap. This automation system is intended to control lights, fans and other electrical appliances in a
home or office using speech commands like Light, Fan etc. Further, if security is not big issue then Speech
processor is used to control the appliances without speaker identification
Intelligent Arabic letters speech recognition system based on mel frequency c...IJECEIAES
Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%.
Emotion Recognition Based On Audio SpeechIOSR Journals
This document summarizes a research paper on emotion recognition based on audio speech. It discusses how acoustic features are extracted from speech signals by applying preprocessing techniques like preemphasis and framing. It describes extracting features like Mel frequency cepstral coefficients (MFCCs) that capture characteristics of the vocal tract. Support vector machines (SVMs) are used as pattern classification methods to build models for each emotion and compare test speech features to recognize emotions. The paper confirms the advantage of its audio-based emotion recognition approach through experimental results and discusses potential improvements and future work on increasing efficiency and recognizing emotion intensity.
1) The document describes research on developing a speech emotion recognition system using convolutional neural networks (CNNs). CNN models are trained on datasets containing speech samples labeled with different emotions.
2) Mel frequency cepstral coefficients are extracted from the speech samples as input features for the CNNs. Various CNN architectures and hyperparameters are tested to improve classification accuracy.
3) The best performing model achieved an accuracy of 77% for classifying emotions among 10 classes using a lighter CNN architecture trained on 80% of data and tested on 20%. Deeper CNN models achieved higher accuracy for classifying among fewer emotion classes.
Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decom...BIJIAM Journal
This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.
Emotion Recognition through Speech Analysis using various Deep Learning Algor...IRJET Journal
This document summarizes a research paper on emotion recognition through speech analysis using deep learning algorithms. The researchers used datasets containing speech samples labeled with seven emotions to train and test convolutional neural network (CNN), support vector machine (SVM), recurrent neural network (RNN), and random forest models. They found that the RNN model achieved the highest testing accuracy at 75.31% for emotion recognition. The researchers concluded that speech emotion recognition systems could be useful for applications like dialogue systems, call centers, student voice reviews, and building healthy relationships.
Voice Recognition Based Automation System for Medical Applications and for Ph...IRJET Journal
This document describes a voice recognition-based automation system for medical applications and physically challenged patients. The system uses a voice recognition model, Arduino microcontroller, relays, LEDs, buzzers, and a motor to control an adjustable bed. Voice commands are recognized using techniques like MFCC and HMM and used to control devices via the Arduino. The system is intended to allow paralyzed patients to control devices like lights, alarms, and their bed using only voice commands for increased independence. Testing showed the system provided accurate voice recognition under various conditions.
Voice Recognition Based Automation System for Medical Applications and for Ph...IRJET Journal
This document describes a voice recognition-based automation system for medical applications and physically challenged patients. The system uses a voice recognition model, Arduino microcontroller, relays, LEDs, buzzers, and a motor to control an adjustable bed. Voice commands are recognized using techniques like MFCC and HMM and used to control devices via the Arduino. The system is intended to allow paralyzed patients to control devices like lights, alarms, and their bed using only voice commands for increased independence. Testing showed the system can accurately recognize commands and control devices with 99% accuracy under suitable conditions.
Classification of Language Speech Recognition Systemijtsrd
This paper is aimed to implement Classification of Language Speech Recognition System by using feature extraction and classification. It is an Automatic language Speech Recognition system. This system is a software architecture which outputs digits from the input speech signals. The system is emphasized on Speaker Dependent Isolated Word Recognition System. To implement this system, a good quality microphone is required to record the speech signals. This system contains two main modules feature extraction and feature matching. Feature extraction is the process of extracting a small amount of data from the voice signal that can later be used to represent each speech signal. Feature matching involves the actual procedure to identify the unknown speech signal by comparing extracted features from the voice input of a set of known speech signals and the decision making process. In this system, the Mel frequency Cepstrum Coefficient MFCC is used for feature extraction and Vector Quantization VQ which uses the LBG algorithm is used for feature matching. Khin May Yee | Moh Moh Khaing | Thu Zar Aung "Classification of Language Speech Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26546.pdfPaper URL: https://www.ijtsrd.com/computer-science/speech-recognition/26546/classification-of-language-speech-recognition-system/khin-may-yee
IRJET- Comparative Analysis of Emotion Recognition SystemIRJET Journal
This document summarizes a research paper that analyzes different machine learning models for speech emotion recognition using the SAVEE dataset. It compares CNN, RFC, XGBoost and SVM classifiers using MFCC features extracted from the audio clips. The CNN model achieved the highest accuracy for emotion recognition when using MFCC features. The paper aims to better understand human emotional states through analyzing dependencies in speech data using these machine learning techniques.
This document reviews techniques used in spoken-word recognition systems. It discusses popular feature extraction techniques like MFCC, LPC, DWT, WPD that are used to represent speech signals in a compact form before classification. Classification techniques discussed are ANN, HMM, DTW, and VQ. The document provides a brief overview of each technique and their advantages. It also presents the generalized workflow of a spoken-word recognition system including stages of speech acquisition, pre-emphasis, feature extraction, modeling, classification, and output of recognized text.
SPEECH EMOTION RECOGNITION SYSTEM USING RNNIRJET Journal
This document discusses a speech emotion recognition system using recurrent neural networks (RNNs). It begins with an abstract describing speech emotion recognition and its importance. Then it provides background on speech emotion databases, feature extraction using MFCC, and classification approaches like RNNs. It reviews related work on speech emotion recognition using various methods. Finally, it concludes that MFCC feature extraction and RNN classification was used in the proposed system to take advantage of their performance in machine learning applications. The system aims to help machines understand human interaction and respond based on the user's emotion.
Similar to Human Emotion Recognition From Speech (20)
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Embedded machine learning-based road conditions and driving behavior monitoring
Human Emotion Recognition From Speech
1. Miss. Aparna P. Wanare Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 4), July 2014, pp.74-78
www.ijera.com 74 | P a g e
Human Emotion Recognition From Speech Miss. Aparna P. Wanare*, Prof. Shankar N. Dandare *(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati University, Amravati) **(Department of Electronics & Telecommunication Engineering, Sant Gadge Baba Amravati University, Amravati) ABSTRACT 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. The proposed system aims at identification of basic emotional states such as anger, joy, neutral and sadness from human speech. While classifying different emotions, features like MFCC (Mel Frequency Cepstral Coefficient) and Energy is used. In this paper, Standard Emotional Database i.e. English Database is used which gives the satisfactory detection of emotions than recorded samples of emotions. This methodology describes and compares the performances of Learning Vector Quantization Neural Network (LVQ NN), Multiclass Support Vector Machine (SVM) and their combination for emotion recognition.
Keywords - Emotion recognition; Feature extraction; Mel-scale Frequency Cepstral Coefficients; Neural Network; Support Vector Machines;
I. INTRODUCTION
Emotion recognition through speech is an area which increasingly attracting attention within the engineers in the field of pattern recognition and speech signal processing in recent years. Emotion recognition plays an important role in identifying emotional state of speaker from voice signal. Emotional speech recognition aims at automatically identifying the emotional or physical state of a human being from his or her voice. The emotional and physical states of a speaker are known as emotional aspects of speech and are included in the so-called paralinguistic aspects [1]. Accurate detection of emotion from speech has clear benefits for the design of more natural human- machine speech interfaces or for the extraction of useful information from large quantities of speech data. It is also becoming more and more important in computer application fields as health care, children education, etc. In speech-based communications, emotion plays an important role [2]. The proposed system aims at identification of basic emotional states such as anger, joy, neutral and sadness from human speech. While classifying different emotions, features like MFCC (Mel Frequency Cepstral Coefficient) and Energy is used. In this paper, Standard Emotional Database i.e. English Database is used which gives the satisfactory
results .This methodology describes and compares the performances of Learning Vector Quantization Neural Network (LVQ NN), Multiclass Support Vector Machine (SVM) and their combination for emotion recognition. The overall experimental results reveal that combination of LVQ NN-SVM has greater accuracy than LVQ NN and SVM.
II. BASIC ARCHITECTURE
The block diagram of the emotion recognition system through speech considered in this study is illustrated in Fig. 1. The block diagram consists of the emotional speech as input, feature extraction, feature selection, classifier and detection of emotion as the output [3]. Fig. 1: Basic Block Diagram of Emotion Recognition
a. Emotional Speech Input: A suitable emotional speech database is important requirement for any emotional recognition model. The quality of database determines the efficiency of the system. The emotional database may contain collection of acted speech or real data world.
RESEARCH ARTICLE OPEN ACCESS
2. Miss. Aparna P. Wanare Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 4), July 2014, pp.74-78
www.ijera.com 75 | P a g e
b. Feature Extraction and Selection: An important
step in emotion recognition system through
speech is to select a significant feature which
carries large emotional information about the
speech signal. After collection of the database
containing emotional speech proper and
necessary features such as prosodic and spectral
features are extracted from the speech signal.
The commonly used features are pitch, energy,
MFCC, LPCC, formant. The steps involved in
calculation of MFCC are shown below.
A. MFCC
Mel-frequency cepstral coefficients (MFCCs) are
coefficients that collectively make up an MFC. They
are derived from a type of cepstral representation of
the audio clip. The difference between the cepstrum
and the mel-frequency cepstrum is that in the MFC,
the frequency bands are equally spaced on the mel
scale, which approximates the human auditory
system's response more closely than the linearly-spaced
frequency bands used in the normal cepstrum.
This frequency warping can allow for better
representation of sound. Fig. 2 shows the MFCC
feature extraction process [4] [5]. As shown in Fig. 2
feature extraction process contains following steps:
Pre-processing: The continuous time signal
(speech) is sampled at sampling frequency. At
the first stage in MFCC feature extraction is to
boost the amount of energy in the high
frequencies. This pre-emphasis is done by using
a filter.
Framing: It is a process of segmenting the speech
samples obtained from analog to digital
conversion (ADC), into the small frames with
the time length within the range of 20-40 msec.
Framing enables the non-stationary speech signal
to be segmented into quasi-stationary frames,
and enables Fourier Transformation of the
speech signal. It is because, speech signal is
known to exhibit quasi-stationary behaviour
within the short time period of 20-40 msec.
Windowing: Windowing step is meant to
window each individual frame, in order to
minimize the signal discontinuities at the
beginning and the end of each frame.
FFT: Fast Fourier Transform (FFT) algorithm is
widely used for evaluating the frequency
spectrum of speech. FFT converts each frame of
N samples from the time domain into the
frequency domain.
Mel Filter bank and Frequency wrapping: The
mel filter bank consists of overlapping triangular
filters with the cut-off frequencies determined by
the centre frequencies of the two adjacent filters.
The filters have linearly spaced centre
frequencies and fixed bandwidth on the mel
scale.
Take Logarithm: The logarithm has the effect of
changing multiplication into addition. Therefore,
this step simply converts the multiplication of
the magnitude in the Fourier transform into
addition.
Take Discrete Cosine Transform: It is used to
orthogonalise the filter energy vectors. Because
of this orthogonalization step, the information of
the filter energy vector is compacted into the first
number of components and shortens the vector to
number of components.
Fig. 2: Block Diagram of the MFCC Feature
Extraction
B. Energy
The Energy is the basic and most important
feature in speech signal. Energy frequently referred
to the volume or intensity of the speech, where it is
also known to contain valuable information. Energy
provides information that can be used to differentiate
sets of emotions, but this measurement alone is not
sufficient to differentiate basic emotions. Joy and
anger have increased energy level, where sadness has
low energy level. Mean of energy is taken into
consideration in proposed emotion recognition
system [6] [7].
En =
C. Classifier
The most important aspect of emotion
recognition system through speech is classification of
an emotion. The performance of system is dependent
on proper choice of classifier. There are many types
of classifier such as Hidden Markov Model (HMM),
Gaussian Mixture Model (GMM), Artificial Neural
Network (ANN) and Support Vector Machine
(SVM).
III. PROPOSED SYSTEM
The ultimate goal of system design should be its
simplicity and efficiency. The Fig. 3 shows
architecture of a speech emotion recognition system.
Generally the speech files are in .mp3 or in .wav
format. For proposed system .wav format files are
used. The English Emotion database contains files in
.wav format. As shown in Fig. 3 system is separated
in two parts. The left hand side represents training
part of system while right hand side represents testing
part. System contains following major blocks:
1. Input speech emotion
2. Feature Extraction
3. Training
3. Miss. Aparna P. Wanare Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 4), July 2014, pp.74-78
www.ijera.com 76 | P a g e
4. Classification Fig. 3: Structure of Speech Emotion Recognition System 3.1 Input As shown in above Fig the input is a .wav file containing emotional speech utterances from English Emotion Database. An English Emotion database is used for training and testing the SVM, LVQ NN and LVQ NN- SVM. The English Emotional Speech (EES) Database expresses four emotional states (happy, sad, angry and neutral) is used for the conduction of the experiment. The basic material of the database consists of „clips‟ extracted from the selected recordings. Clips ranged from 3-8 sec‟s in length. An audio file (.wav format) contains speech alone, edited to remove sounds other than the main speaker. Here some samples are used for training the system and then remaining samples are used for testing purpose. 3.2 Feature Extraction It is an important step in emotion recognition system to extract the features which contains maximum information related to human emotions. A proper selection of set of features can increase efficiency of system. In this paper two features are taken into consideration and extracted from audio samples they are MFCC and Energy. The steps involved in calculation of MFCC are show above. 3.3 Classifier Support Vector Machine
The Support Vector Machine is used as a classifier for emotion recognition. The SVM is computer algorithm used in pattern recognition for data classification and regression. The classifier is used for classifying or separating the features from other features. Support Vector Machine performs classification by constructing an N-dimensional hyper-plane that optimally separates the data into categories. A good separation is achieved by the hyperplane that has the largest distance to the nearest training data point of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.The classification is achieved by a linear or nonlinear separating surface in the input space of the dataset [8] [9]. The SVM is a binary classifier but with some approaches it can be used as multiclass classifier. Two common methods to build binary classifiers are where each classifier distinguishes between (i) one of the labels to the rest (one-versus-all) or (ii) between every pair of classes (one-versus-one). Classification of new instances for one-versus-all case is done by winner takes- all strategy, in which the classifiers with the highest output function assign the class. The classification of one-versus-one case is done by max-wins voting strategy, in which every classifier assign the instance to one of the two classes, then the vote for the assigned class is increased by one vote. Finally the class with most votes determines the instance classification. The Fig. 4 for one-versus-all and Fig. 5 for one-versus-one is shown below. Fig. 4: One-versus-all Approach Fig. 5: One-versus-one Approach In this paper classification is carried out with help of Multiclass classifier i.e. one-to-one multiclass approach, in which every classifier assigns the instance to one of the two classes, then the vote for the assigned class is increased by one vote, and finally the class with the most votes determines the instance classification. The one-versus-one (1v1) classifier uses a „max-wins‟ voting strategy. It constructs m(m − 1)/2 binary classifiers, one for every pair of distinct classes. Each binary classifier Ci j is trained on the data from the i th and j th classes only. For a given test sample, if classifier Ci j predicts it is in class i , then the vote for class i is increased by one; otherwise the vote for class j is increased by one. Then the „max-wins‟ voting strategy assigns the test sample to the highest scoring class.
4. Miss. Aparna P. Wanare Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 4), July 2014, pp.74-78
www.ijera.com 77 | P a g e
Learning Vector Quantization LVQ can be understood as a special case of an artificial neural network, more precisely; it applies a winner-take-all Hebbian learning based approach. It is a precursor to Self-organizing map and LVQ was invented by Kohonen. The LVQ network architecture is shown below in Fig. 6. Fig. 6: LVQ Network Architecture Where R= number of elements of input vector S1= number of competitive neurons S2= number of linear neurons An LVQ network has a first competitive layer and a second linear layer. The competitive layer learns to classify input vectors in much the same way as the competitive layers of Self-Organizing Map. The linear layer transforms the competitive layer's classes into target classifications defined by the user. Learning Vector Quantization (LVQ) is a neural net that combines competitive learning with supervision. It can be used for pattern classification. Learning Vector Quantization (LVQ) is a method for training competitive layers in a supervised manner. A competitive layer automatically learns to classify input vectors. However, the classes that the competitive layer finds are dependent only on the distance between input vectors. If two input vectors are very similar, the competitive layer probably will put them in the same class. There is no mechanism in a strictly competitive layer design to say whether or not any two input vectors are in the same class or different classes. LVQ networks, on the other hand, learn to classify input vectors into target classes chosen by the user. In the competitive layer, neuron in the first layer learns a prototype vector which allows it to classify a region of the input space. Hybrid Method (SVM-LVQ NN)
In this paper, a combination of Support vector Machine (SVM) and Learning Vector Quantization (LVQ) is proposed. This paper clears that the result can be enhance by combining the properties of SVM and LVQ NN. An advantage of LVQ is that it creates prototypes that are easy to interpret for experts in the respective application domain. LVQ systems can be applied to multi-class classification problems in a natural way. A competitive layer automatically learns to classify input vectors. LVQ networks, on the other hand, learn to classify input vectors into target classes chosen by the user.Support Vector Machine performs classification by constructing an N-dimensional hyper-plane that optimally separates the data into categories. It is one of the best methods for classification of emotions from speech. Hence we proposed a system based on combination of LVQ NN and SVM.A comparative analysis of result of single NN, single SVM and hybrid model of both is done.
IV. RESULT AND CONCLUSION
Result For comparison of results using three different approach graphical representations is shown in following figure. The result obtained using hybrid combination is comparatively superior to remaining two methods for each emotion. The overall accuracy of proposed method is boosted by 6-10%. Fig. 7: Percent Accuracy of each Emotion in NN, SVM and Hybrid Model Fig. 8: Percent Overall Accuracy of each Model
V. CONCLUSION
Recognition of emotional states from speech is a current research topic with wide range. Emotion recognition through speech is particularly useful for applications in the field of human machine interaction to make better human machine interface. It is gaining a lot of importance due to its wide range of application in day to day life.
In this paper, the features to be extracted are MFCC and Energy from English Emotion Database. The emotion recognition accuracy using LVQ NN is 71.94% whereas by multiclass SVM is 77.57%. The proposed method i.e. hybrid model is designed and implemented. The hybrid model i.e. LVQ NN-SVM
5. Miss. Aparna P. Wanare Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 7( Version 4), July 2014, pp.74-78
www.ijera.com 78 | P a g e
yields better accuracy i.e. 83.68% than other two methods. From the result it can be concluded that the proposed method LVQ NN-SVM yields better result than LVQ NN and SVM and has been successfully implemented. REFERENCES
[1] Prof. Sujata Pathak, Prof. Arun Kulkarni, “Recognizing Emotions from Speech”, 3rd International conference, Vol.:6, pp.107- 109, IEEE 2011.
[2] Vaishali M. Chavan, V. V. Gohokar, “Speech Emotion Recognition by using SVM-Classifier”, International Journal of Engineering and Advanced Technology (IJEAT), Vol: 1, Issue: 5, pp.11-15, ISSN: 2249-8958, June 2012.
[3] Dipti D. Joshi1, Prof. M. B. Zalte, “Speech Emotion Recognition: A Review” IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), Volume 4, Issue 4 ,Pp 34-37, ISSN: 2278-2834, (Jan. - Feb. 2013)
[4] Bhoomika Panda, Debananda Padhi, Kshamamayee Dash, Prof. Sanghamitra Mohanty, “Use of SVM Classifier & MFCC in Speech Emotion Recognition System”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, Issue 3, pp.226-230, ISSN:2277-128X, March 2012.
[5] Sujata B. Wankhade, Pritish Tijare, Yashpalsing Chavhan, “Speech Emotion Recognition System Using SVM AND LIBSVM”, International Journal of Computer Science And Applications Vol. 4, No. 2, pp.89-96, ISSN: 0974-1003, July 2011.
[6] Yixiong Pan, Peipei Shen and Liping Shen ,“Speech Emotion Recognition Using Support Vector Machine”, International Journal of Smart Home Vol. 6, No. 2, pp.101-108, April 2012.
[7] Mohammad Masoud Javidi and Ebrahim Fazlizadeh Roshan, “Speech Emotion Recognition by Using Combinations of C5.0, Neural Network (NN), and Support Vector Machines (SVM) Classification Methods”, Journal of mathematics and computer Science, Vol.: 6, Issue: 3, pp.191-200, 2013.
[8] A. Milton, S. Sharmy Roy, S. Tamil Selvi, “SVM Scheme for Speech Emotion Recognition using MFCC Feature”, International Journal of Computer Applications, Vol. 69 – No. 9, pp. 34-40, May 2013.
[9] Thapanee Seehapoch, Sartra Wongthanavasu, “Speech Emotion
Recognition Using Support Vector Machines”, 5th International Conference on Knowledge and Smart Technology, pp. 86- 91, 2013.