IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
ASERS-CNN: ARABIC SPEECH EMOTION RECOGNITION SYSTEM BASED ON CNN MODELsipij
When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metapho...Sunkyung Lee
This is the official slide for the NAACL 2021 paper: MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories.
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.
ASERS-LSTM: Arabic Speech Emotion Recognition System Based on LSTM Modelsipij
The swift progress in the study field of human-computer interaction (HCI) causes to increase in the interest in systems for Speech emotion recognition (SER). The speech Emotion Recognition System is the system that can identify the emotional states of human beings from their voice. There are well works in Speech Emotion Recognition for different language but few researches have implemented for Arabic SER systems and that because of the shortage of available Arabic speech emotion databases. The most commonly considered languages for SER is English and other European and Asian languages. Several machine learning-based classifiers that have been used by researchers to distinguish emotional classes: SVMs, RFs, and the KNN algorithm, hidden Markov models (HMMs), MLPs and deep learning. In this paper we propose ASERS-LSTM model for Arabic Speech Emotion Recognition based on LSTM model. We extracted five features from the speech: Mel-Frequency Cepstral Coefficients (MFCC) features, chromagram, Melscaled spectrogram, spectral contrast and tonal centroid features (tonnetz). We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we also construct a DNN for classify the Emotion and compare the accuracy between LSTM and DNN model. For DNN the accuracy is 93.34% and for LSTM is 96.81%
ASERS-CNN: ARABIC SPEECH EMOTION RECOGNITION SYSTEM BASED ON CNN MODELsipij
When two people are on the phone, although they cannot observe the other person's facial expression and physiological state, it is possible to estimate the speaker's emotional state by voice roughly. In medical care, if the emotional state of a patient, especially a patient with an expression disorder, can be known, different care measures can be made according to the patient's mood to increase the amount of care. The system that capable for recognize the emotional states of human being from his speech is known as Speech emotion recognition system (SER). Deep learning is one of most technique that has been widely used in emotion recognition studies, in this paper we implement CNN model for Arabic speech emotion recognition. We propose ASERS-CNN model for Arabic Speech Emotion Recognition based on CNN model. We evaluated our model using Arabic speech dataset named Basic Arabic Expressive Speech corpus (BAES-DB). In addition of that we compare the accuracy between our previous ASERS-LSTM and new ASERS-CNN model proposed in this paper and we comes out that our new proposed mode is outperformed ASERS-LSTM model where it get 98.18% accuracy
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metapho...Sunkyung Lee
This is the official slide for the NAACL 2021 paper: MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories.
Speaker Identification From Youtube Obtained Datasipij
An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like
YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker
identification is to identify the number of different speakers and prepare a model for that speaker by
extraction, characterization and speaker-specific information contained in the speech signal. It has many
diverse application specially in the field of Surveillance , Immigrations at Airport , cyber security ,
transcription in multi-source of similar sound source, where it is difficult to assign transcription arbitrary.
The most commonly speech parameterization used in speaker verification, K-mean, cepstral analysis, is
detailed. Gaussian mixture modeling, which is the speaker modeling technique is then explained. Gaussian
mixture models (GMM), perhaps the most robust machine learning algorithm has been introduced to
examine and judge carefully speaker identification in text independent. The application or employment of
Gaussian mixture models for monitoring & Analysing speaker identity is encouraged by the familiarity,
awareness, or understanding gained through experience that Gaussian spectrum depict the characteristics
of speaker's spectral conformational pattern and remarkable ability of GMM to construct capricious
densities after that we illustrate 'Expectation maximization' an iterative algorithm which takes some
arbitrary value in initial estimation and carry on the iterative process until the convergence of value is
observed We have tried to obtained 85 ~ 95% of accuracy using speaker modeling of vector quantization
and Gaussian Mixture model ,so by doing various number of experiments we are able to obtain 79 ~ 82%
of identification rate using Vector quantization and 85 ~ 92.6% of identification rate using GMM modeling
by Expectation maximization parameter estimation depending on variation of parameter.
This is the presentation of our IEEE ICASSP 2021 paper "seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset".
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%.
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.
Marathi Isolated Word Recognition System using MFCC and DTW FeaturesIDES Editor
This paper presents a Marathi database and isolated
Word recognition system based on Mel-frequency cepstral
coefficient (MFCC), and Distance Time Warping (DTW) as
features. For the extraction of the feature, Marathi speech
database has been designed by using the Computerized Speech
Lab. The database consists of the Marathi vowels, isolated
words starting with each vowels and simple Marathi sentences.
Each word has been repeated three times by the 35 speakers.
This paper presents the comparative recognition accuracy of
DTW and MFCC.
Emotion expression is an essential function for dai
ly life that can be severely affected some psycholo
gical
disorders. In this paper we identified seven emotio
nal states anger,surprise,sadness ,happiness,fear,d
isgust
and neutral.The definition of parameters is a cruci
al step in the development of a system for emotion
analysis.The 15 explored features are energy intens
ity,pitch,standard
deviation,jitter,shimmer,autocorrelation,noise to h
armonic ration,harmonic to noise ration,energy entr
opy
block,short term energy,zero crossing rate,spectral
roll-off,spectral centroid and spectral flux,and f
ormants
In this work database used is SAVEE(Surrey audio vi
sual expressed emotion).Results by using different
learning methods and estimation is done by using a
confidence interval for identified parameters are
compared and explained.The overall experimental res
ults reveals that Model 2 and Model 3 give better
results than Model 1 using learning methods and es
timation shows that most emotions are correctly
estimated by using energy intensity and pitch.
EFFECT OF MFCC BASED FEATURES FOR SPEECH SIGNAL ALIGNMENTSijnlc
The fundamental techniques used for man-machine communication include Speech synthesis, speech
recognition, and speech transformation. Feature extraction techniques provide a compressed
representation of the speech signals. The HNM analyses and synthesis provides high quality speech with
less number of parameters. Dynamic time warping is well known technique used for aligning two given
multidimensional sequences. It locates an optimal match between the given sequences. The improvement in
the alignment is estimated from the corresponding distances. The objective of this research is to investigate
the effect of dynamic time warping on phrases, words, and phonemes based alignments. The speech signals
in the form of twenty five phrases were recorded. The recorded material was segmented manually and
aligned at sentence, word, and phoneme level. The Mahalanobis distance (MD) was computed between the
aligned frames. The investigation has shown better alignment in case of HNM parametric domain. It has
been seen that effective speech alignment can be carried out even at phrase level.
Deep neural networks have shown recent promise in many language-related tasks such as the modelling of
conversations. We extend RNN-based sequence to sequence models to capture the long-range discourse
across many turns of conversation. We perform a sensitivity analysis on how much additional context
affects performance, and provide quantitative and qualitative evidence that these models can capture
discourse relationships across multiple utterances. Our results show how adding an additional RNN layer
for modelling discourse improves the quality of output utterances and providing more of the previous
conversation as input also improves performance. By searching the generated outputs for specific
discourse markers, we show how neural discourse models can exhibit increased coherence and cohesion in
conversations.
Deep neural networks have shown recent promise in many language-related tasks such as the modelling of conversations. We extend RNN-based sequence to sequence models to capture the long-range discourse across many turns of conversation. We perform a sensitivity analysis on how much additional context affects performance, and provide quantitative and qualitative evidence that these models can capture discourse relationships across multiple utterances. Our results show how adding an additional RNN layer for modelling discourse improves the quality of output utterances and providing more of the previous conversation as input also improves performance. By searching the generated outputs for specific discourse markers, we show how neural discourse models can exhibit increased coherence and cohesion in conversations.
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.
EFFECT OF DYNAMIC TIME WARPING ON ALIGNMENT OF PHRASES AND PHONEMESkevig
Speech synthesis and recognition are the basic techniques used for man-machine communication. This type
of communication is valuable when our hands and eyes are busy in some other task such as driving a
vehicle, performing surgery, or firing weapons at the enemy. Dynamic time warping (DTW) is mostly used
for aligning two given multidimensional sequences. It finds an optimal match between the given sequences.
The distance between the aligned sequences should be relatively lesser as compared to unaligned
sequences. The improvement in the alignment may be estimated from the corresponding distances. This
technique has applications in speech recognition, speech synthesis, and speaker transformation. The
objective of this research is to investigate the amount of improvement in the alignment corresponding to the
sentence based and phoneme based manually aligned phrases. The speech signals in the form of twenty five
phrases were recorded from each of six speakers (3 males and 3 females). The recorded material was
segmented manually and aligned at sentence and phoneme level. The aligned sentences of different speaker
pairs were analyzed using HNM and the HNM parameters were further aligned at frame level using DTW.
Mahalanobis distances were computed for each pair of sentences. The investigations have shown more than
20 % reduction in the average Mahalanobis distances.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Speaker Identification From Youtube Obtained Datasipij
An efficient, and intuitive algorithm is presented for the identification of speakers from a long dataset (like
YouTube long discussion, Cocktail party recorded audio or video).The goal of automatic speaker
identification is to identify the number of different speakers and prepare a model for that speaker by
extraction, characterization and speaker-specific information contained in the speech signal. It has many
diverse application specially in the field of Surveillance , Immigrations at Airport , cyber security ,
transcription in multi-source of similar sound source, where it is difficult to assign transcription arbitrary.
The most commonly speech parameterization used in speaker verification, K-mean, cepstral analysis, is
detailed. Gaussian mixture modeling, which is the speaker modeling technique is then explained. Gaussian
mixture models (GMM), perhaps the most robust machine learning algorithm has been introduced to
examine and judge carefully speaker identification in text independent. The application or employment of
Gaussian mixture models for monitoring & Analysing speaker identity is encouraged by the familiarity,
awareness, or understanding gained through experience that Gaussian spectrum depict the characteristics
of speaker's spectral conformational pattern and remarkable ability of GMM to construct capricious
densities after that we illustrate 'Expectation maximization' an iterative algorithm which takes some
arbitrary value in initial estimation and carry on the iterative process until the convergence of value is
observed We have tried to obtained 85 ~ 95% of accuracy using speaker modeling of vector quantization
and Gaussian Mixture model ,so by doing various number of experiments we are able to obtain 79 ~ 82%
of identification rate using Vector quantization and 85 ~ 92.6% of identification rate using GMM modeling
by Expectation maximization parameter estimation depending on variation of parameter.
This is the presentation of our IEEE ICASSP 2021 paper "seen and unseen emotional style transfer for voice conversion with a new emotional speech dataset".
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%.
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.
Marathi Isolated Word Recognition System using MFCC and DTW FeaturesIDES Editor
This paper presents a Marathi database and isolated
Word recognition system based on Mel-frequency cepstral
coefficient (MFCC), and Distance Time Warping (DTW) as
features. For the extraction of the feature, Marathi speech
database has been designed by using the Computerized Speech
Lab. The database consists of the Marathi vowels, isolated
words starting with each vowels and simple Marathi sentences.
Each word has been repeated three times by the 35 speakers.
This paper presents the comparative recognition accuracy of
DTW and MFCC.
Emotion expression is an essential function for dai
ly life that can be severely affected some psycholo
gical
disorders. In this paper we identified seven emotio
nal states anger,surprise,sadness ,happiness,fear,d
isgust
and neutral.The definition of parameters is a cruci
al step in the development of a system for emotion
analysis.The 15 explored features are energy intens
ity,pitch,standard
deviation,jitter,shimmer,autocorrelation,noise to h
armonic ration,harmonic to noise ration,energy entr
opy
block,short term energy,zero crossing rate,spectral
roll-off,spectral centroid and spectral flux,and f
ormants
In this work database used is SAVEE(Surrey audio vi
sual expressed emotion).Results by using different
learning methods and estimation is done by using a
confidence interval for identified parameters are
compared and explained.The overall experimental res
ults reveals that Model 2 and Model 3 give better
results than Model 1 using learning methods and es
timation shows that most emotions are correctly
estimated by using energy intensity and pitch.
EFFECT OF MFCC BASED FEATURES FOR SPEECH SIGNAL ALIGNMENTSijnlc
The fundamental techniques used for man-machine communication include Speech synthesis, speech
recognition, and speech transformation. Feature extraction techniques provide a compressed
representation of the speech signals. The HNM analyses and synthesis provides high quality speech with
less number of parameters. Dynamic time warping is well known technique used for aligning two given
multidimensional sequences. It locates an optimal match between the given sequences. The improvement in
the alignment is estimated from the corresponding distances. The objective of this research is to investigate
the effect of dynamic time warping on phrases, words, and phonemes based alignments. The speech signals
in the form of twenty five phrases were recorded. The recorded material was segmented manually and
aligned at sentence, word, and phoneme level. The Mahalanobis distance (MD) was computed between the
aligned frames. The investigation has shown better alignment in case of HNM parametric domain. It has
been seen that effective speech alignment can be carried out even at phrase level.
Deep neural networks have shown recent promise in many language-related tasks such as the modelling of
conversations. We extend RNN-based sequence to sequence models to capture the long-range discourse
across many turns of conversation. We perform a sensitivity analysis on how much additional context
affects performance, and provide quantitative and qualitative evidence that these models can capture
discourse relationships across multiple utterances. Our results show how adding an additional RNN layer
for modelling discourse improves the quality of output utterances and providing more of the previous
conversation as input also improves performance. By searching the generated outputs for specific
discourse markers, we show how neural discourse models can exhibit increased coherence and cohesion in
conversations.
Deep neural networks have shown recent promise in many language-related tasks such as the modelling of conversations. We extend RNN-based sequence to sequence models to capture the long-range discourse across many turns of conversation. We perform a sensitivity analysis on how much additional context affects performance, and provide quantitative and qualitative evidence that these models can capture discourse relationships across multiple utterances. Our results show how adding an additional RNN layer for modelling discourse improves the quality of output utterances and providing more of the previous conversation as input also improves performance. By searching the generated outputs for specific discourse markers, we show how neural discourse models can exhibit increased coherence and cohesion in conversations.
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.
EFFECT OF DYNAMIC TIME WARPING ON ALIGNMENT OF PHRASES AND PHONEMESkevig
Speech synthesis and recognition are the basic techniques used for man-machine communication. This type
of communication is valuable when our hands and eyes are busy in some other task such as driving a
vehicle, performing surgery, or firing weapons at the enemy. Dynamic time warping (DTW) is mostly used
for aligning two given multidimensional sequences. It finds an optimal match between the given sequences.
The distance between the aligned sequences should be relatively lesser as compared to unaligned
sequences. The improvement in the alignment may be estimated from the corresponding distances. This
technique has applications in speech recognition, speech synthesis, and speaker transformation. The
objective of this research is to investigate the amount of improvement in the alignment corresponding to the
sentence based and phoneme based manually aligned phrases. The speech signals in the form of twenty five
phrases were recorded from each of six speakers (3 males and 3 females). The recorded material was
segmented manually and aligned at sentence and phoneme level. The aligned sentences of different speaker
pairs were analyzed using HNM and the HNM parameters were further aligned at frame level using DTW.
Mahalanobis distances were computed for each pair of sentences. The investigations have shown more than
20 % reduction in the average Mahalanobis distances.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Surge de la necesidad de las pequeñas y medianas empresas asociadas a AJE, que demandan desde hace un tiempo asesoramiento experto en temas de innovación empresarial, abarcando diferentes ámbitos de la empresa, con el fin de mejorar su competitividad y ampliar su mercado actual mediante la internacionalización de su oferta de productos y servicios.
Se pretende potenciar el crecimiento y desarrollo de las pymes participantes mediante la elaboración de un Plan Director de Innovación e Internacionalización que incremente su capacidad tecnológica y de atractivo en el mercado nacional e internacional, que mediante un ejercicio de reflexión estratégica, incorpore la gestión de la innovación en la planificación empresarial.
La innovación es aplicable a cualquier sector, industria, comercio, turismo, servicios…
Las sinergias se pueden establecer en cualquier sector y mercado, sea emergente, maduro, tradicional, tecnológico…
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Effect of MFCC Based Features for Speech Signal Alignmentskevig
The fundamental techniques used for man-machine communication include Speech synthesis, speech
recognition, and speech transformation. Feature extraction techniques provide a compressed
representation of the speech signals. The HNM analyses and synthesis provides high quality speech with
less number of parameters. Dynamic time warping is well known technique used for aligning two given
multidimensional sequences. It locates an optimal match between the given sequences. The improvement in
the alignment is estimated from the corresponding distances. The objective of this research is to investigate
the effect of dynamic time warping on phrases, words, and phonemes based alignments. The speech signals
in the form of twenty five phrases were recorded. The recorded material was segmented manually and
aligned at sentence, word, and phoneme level. The Mahalanobis distance (MD) was computed between the
aligned frames. The investigation has shown better alignment in case of HNM parametric domain. It has
been seen that effective speech alignment can be carried out even at phrase level
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.
Signal & Image Processing : An International Journal sipij
Signal & Image Processing : An International Journal is an Open Access peer-reviewed journal intended for researchers from academia and industry, who are active in the multidisciplinary field of signal & image processing. The scope of the journal covers all theoretical and practical aspects of the Digital Signal Processing & Image processing, from basic research to development of application.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
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.
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.
Effect of Time Derivatives of MFCC Features on HMM Based Speech Recognition S...IDES Editor
In this paper, improvement of an ASR system for
Hindi language, based on Vector quantized MFCC as feature
vectors and HMM as classifier, is discussed. MFCC features
are usually pre-processed before being used for recognition.
One of these pre-processing is to create delta and delta-delta
coefficients and append them to MFCC to create feature vector.
This paper focuses on all digits in Hindi (Zero to Nine), which
is based on isolated word structure. Performance of the system
is evaluated by accurate Recognition Rate (RR). The effect of
the combination of the Delta MFCC (DMFCC) feature along
with the Delta-Delta MFCC (DDMFCC) feature shows
approximately 2.5% further improvement in the RR, with no
additional computational costs involved. RR of the system for
the speakers involved in the training phase is found to give
better recognition accuracy than that for the speakers who
were not involved in the training phase. Word wise RR is
observed to be good in some digits with distinct phones.
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.
Speaker recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. Voice -recognition is combination of the two where it uses learned aspects of a speaker’s voice to determine what is being said - such a system cannot recognize speech from random speakers very accurately, but it can reach high accuracy for individual voices it has been trained with, which gives us various applications in day today life.
Over the years speech recognition has taken the market. The speech input can be used in varying domains such as automatic reader and for inputting data to the system. Speech recognition can minimize the use of text and other types of input, at the same time minimizing the calculation needed for the process. Decade back speech recognition was difficult to use in any system, but with elevation in technology leading to new algorithms, techniques and advanced tools. Now it is possible to generate the desired speech recognition output. One such method is the hidden markov models which is used in this paper. Voice or signaled input is inserted through any speech device such as microphone, then speech can be processed and convert it to text hence able to send SMS, also Phone number can be entering either by voice or you may select it from contact list. Voice has opened up data input for a variety of user’s such as illiterate, Handicapped, as if the person cannot write then the speech input is a boon and other’s too which Can lead to better usage of the application. This application also included that user can only input numeric character for contact information, i.e. the security validation for number is done. SR will listen to input and convert numeric to text and will be displayed on contact information to verify. If any user try to insert any other character into the information an error would be displayed e.g. if user speaks his name for contact, it will be displayed as invalid contact. The message box can accept any character. To use the speech recognition user has to be loud and clear so that command is properly executed by the system.
1. K. Ravi Kumar, V.Ambika, K.Suri Babu / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1797-1799
Emotion Identification From Continuous Speech Using Cepstral
Analysis
K. Ravi Kumar1, V.Ambika2, K.Suri Babu3
1
M.tech(DECS), Chaitanya Engineering College, Visakhapatnam, India.
2
Dept of ECE, Chaitanya Engineering College, Visakhapatnam, India.
3
Scientist, NSTL (DRDO), Govt. of India, Visakhapatnam, India
ABSTRACT
Emotion plays a major role in the area 2. FEATURE EXTRACTION
of psychology, human computer interaction, and In order to have an effective recognition
robotics and BPO sectors. With the system, the features are to be extracted efficiently.
advancements in the field of communication In order to achieve this, we convert these speech
technology, it is possible to establish the channel signals and model it by using Gamma mixture
within few seconds across the globe. As most of model. Every speech signal varies gradually in slow
the communication channels are public data phase and its features are fairly constant .In order to
transmission may not be authenticated. In such identify the features, long speech duration is to be
situation, before interacting, it is essential to considered. Features like MFCC and LPCs are most
recognize speaker by the unique features in the commonly used, The main advantage of MFCC is, it
speech. A speaker can modulate his/her voice can tries to identify the features in the presence of noise,
changes his/her emotion state. Hence emotion and LPCs are mostly preferred to extract the
recognition is required for the applications like features in low acoustics, LPC and
telemetry, call centers, forensics and security. In MFCC[8]coefficients are used to extract the features
our project the main emotion consider happy, from the given speech.
angry, boredom, and sad. In this work we dealt
with speaker recognition with different emotion. Speech signal
The basic emotions for this study include angry,
sad, happy, boredom and neutral. The features
we modeled using Gamma Distribution(GD) and
data base generated with 50 speakers of both
genders with the above basic emotions
Considering feature vector combinations
MFCC-LPC.
Keywords: MFCC, LPC, Gamma Distribution
1. INTRODUCTION
In some specific situations, such as remote
medical treatment, call center applications, it is very
much necessary to identify the speaker along with
his/her emotion. Hence in this paper a methodology
for emotion identification in continuous speech, the
various emotions consider are happy, angry, sad,
boredom and neutral. Lot of research is projected to
recognize the emotional states of the speaker using
various models such as GMM, HMM, SVM, neural
networks [2][3][4][5]. In this paper we have
considered an emotional data base with 50 speakers
of both genders. The data is trained and for
classification of the speakers emotions generalized
gamma distribution is utilized. The data is tested
with different emotions .The rest of the paper is
organized as follows. In section-2 of the paper deals
with feature extraction, .section-3 of the paper
generalized gamma distribution is presented.section- Mel Cepstrum
4 of the paper deals with the experimentation and
results.
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2. K. Ravi Kumar, V.Ambika, K.Suri Babu / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 5, September- October 2012, pp.1797-1799
3. GENERALIZED GAMMA MIXTURE Cluster the recorded speech of unknown
MODEL emotion for testing using fuzzy c-means clustering
Today most of the research in speech technique and perform step2 to step 3.
processing is carried out by using Gaussian mixture Step5:
model, but the main disadvantage with GMM is that Find the range of speech of test signal in
it relies exclusively on the the approximation and the trained set.
low in convergence, and also if GMM is used the Step6:
speech and the noise coefficients differ in magnitude Evaluation metrics such as Acceptance
[9]. To have a more accurate feature extraction Rate (AR), False Acceptance Rate (FAR),
maximum posterior estimation models are to be Missed Detection Rate(MDR) are calculated to find
considered [10].Hence in this paper generalized the accuracy of speaker recognition.
gamma distribution is utilized for classifying the
speech signal. Generalized gamma distribution 5. EXPERIMENTATION
represents the sum of n-exponential distributed In general, the Emotion signal will always
random variables both the shape and scale be of finite range and therefore, it needs to truncate
parameters have non-negative integer values [11]. the infinite range. Hence it is always advantage to
Generalized gamma distribution is defined in terms consider the truncations of GMM into finite range;
of scale and shape parameters [12]. The generalized also it is clearly observed that the pitch signals along
gamma mixture is given by the right side are more appropriate. Hence, in this
paper we have considered Generalized Gamma
(1) Distribution with acted sequences of 5 different
emotions, namely happy, sad, angry, boredom,
neutral. In order to test the data 50 samples are
Where k and c are the shape parameters, a considered and a database of audio voice is
is the location parameter, b is the scale parameter generated in .wav format. The emotion speech data
and gamma is the complete gamma function [13]. base is considered with different emotions such as
The shape and scale parameters of the generalized happy, angry, sad, boredom and neutral. The data
gamma distribution help to classify the speech base is generated from the voice samples of both the
signal and identify the speaker accurately. genders .50 samples have been recorded using text
dependent data; we have considered only a short
4. OUR APPROACH OF SPEAKER sentence. The data is trained by extracting the voice
EMOTION RECOGNITION features MFCC and LPC. The data is recorded with
For identifying emotion in a continuous sampling rate of 16 kHz. The signals were divided
speech, our method considers MFCC-LPC as feature into 256 frames with an overlap of 128 frames and
vector. Emotions of speaker identified using various the MFCC and LPC for each frame is computed .In
sets of recorded. Unknown emotion recognition of order to classify the emotions and to appropriately
speaker considering continuous speech. Every identify the speaker generalized gamma distribution
speech consists of the basic emotions: Angry, is considered .The experimentation has been
happy, Boredom, neutral, sad to identify emotional conducted on the database, by considering 10
state of speaker, we should train the speaker’s emotions per training and 5 emotions for testing .we
speech properly i.e. selection of feature vector is have repeated the experimentation and above 90%
crucial in emotion identification. Recorded a over all recognition rate is achieved. The
continuous speech with unknown emotion .Known experimentation is conducted by changing deferent
emotions are used for training. The steps to be emotions and testing the data with a speaker’s voice
followed for identification of emotion in continuous .In all the cases the recognition rate is above 90%.
speech effectively are given under
6. CONCLUSIONS:
Step1: In this paper a novel frame work for
Obtain the training set by recording the emotion identification in continuous speech with
speech voices in a .wav form MFCC and LPC together as feature vectors. This
Step2: work is very much useful in applications such as
Identify the feature vector of these speech speaker identification associated with emotional
signals by application of transformation based coefficients .It is used in practical situations such as
compound feature vector MFCC-LPC. call centers and telemedicine. In this paper
Step3: generalized gamma distribution is considered for
Generate the probability density function classification and over all recognition rate of above
(PDF) of the generalized gamma distribution for all 90% is achieved.
the trained data set.
Step4:
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