Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
speech recognition,History of speech recognition,what is speech recognition,Voice recognition software , Advantages and Disadvantages speech recognition, voice recognition,Voice recognition in operating systems ,Types of speech recognition
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
speech recognition,History of speech recognition,what is speech recognition,Voice recognition software , Advantages and Disadvantages speech recognition, voice recognition,Voice recognition in operating systems ,Types of speech recognition
Facial Emotion Recognition: A Deep Learning approachAshwinRachha
Neural Networks lie at the apogee of Machine Learning algorithms. With a large set of data and automatic feature selection and extraction process, Convolutional Neural Networks are second to none. Neural Networks can be very effective in classification problems.
Facial Emotion Recognition is a technology that helps companies and individuals evaluate customers and optimize their products and services by most relevant and pertinent feedback.
Facial emotion detection on babies' emotional face using Deep Learning.Takrim Ul Islam Laskar
phase- 1
Face Detection.
Facial Landmark detection.
phase- 2
Neural Network Training and Testing.
validation and implementation.
phase - 1 has been completed successfully.
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
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.
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.
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.
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%.
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 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.
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.
Malayalam Isolated Digit Recognition using HMM and PLP cepstral coefficientijait
Development of Malayalam speech recognition system is in its infancy stage; although many works have been done in other Indian languages. In this paper we present the first work on speaker independent Malayalam isolated speech recognizer based on PLP (Perceptual Linear Predictive) Cepstral Coefficient and Hidden Markov Model (HMM). The performance of the developed system has been evaluated with different number of states of HMM (Hidden Markov Model). The system is trained with 21 male and female speakers in the age group ranging from 19 to 41 years. The system obtained an accuracy of 99.5% with the unseen data.
Broad Phoneme Classification Using Signal Based Features ijsc
Speech is the most efficient and popular means of human communication Speech is produced as a sequence of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme classification is achieved using features derived directly from the speech at the signal level itself. Broad phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first three formants. Features derived from short time frames of training speech are used to train a multilayer feedforward neural network based classifier with manually marked class label as output and classification accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction which is useful for applications such as automatic speech recognition and automatic language identification.
Speaker and Speech Recognition for Secured Smart Home ApplicationsRoger Gomes
The paper published in discusses implementation of a robust text-independent speaker recognition system using MFCC extraction of feature vectors its matching using VQ and optimization using LBG, further a text dependent speech recognition system using the DTW algorithm's implementation is discussed in the context of home automation.
Broad phoneme classification using signal based featuresijsc
Speech is the most efficient and popular means of human communication Speech is produced as a sequence
of phonemes. Phoneme recognition is the first step performed by automatic speech recognition system. The
state-of-the-art recognizers use mel-frequency cepstral coefficients (MFCC) features derived through short
time analysis, for which the recognition accuracy is limited. Instead of this, here broad phoneme
classification is achieved using features derived directly from the speech at the signal level itself. Broad
phoneme classes include vowels, nasals, fricatives, stops, approximants and silence. The features identified
useful for broad phoneme classification are voiced/unvoiced decision, zero crossing rate (ZCR), short time
energy, most dominant frequency, energy in most dominant frequency, spectral flatness measure and first
three formants. Features derived from short time frames of training speech are used to train a multilayer
feedforward neural network based classifier with manually marked class label as output and classification
accuracy is then tested. Later this broad phoneme classifier is used for broad syllable structure prediction
which is useful for applications such as automatic speech recognition and automatic language
identification.
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.
Isolated word recognition using lpc & vector quantizationeSAT Journals
Abstract Speech recognition is always looked upon as a fascinating field in human computer interaction. It is one of the fundamental steps towards understanding human recognition and their behavior. This paper explicates the theory and implementation of Speech recognition. This is a speaker-dependent real time isolated word recognizer. The major logic used was to first obtain the feature vectors using LPC which was followed by vector quantization. The quantized vectors were then recognized by measuring the Minimum average distortion. All Speech Recognition systems contain Two Main Phases, namely Training Phase and Testing Phase. In the Training Phase, the Features of the words are extracted and during the recognition phase feature matching Takes place. The feature or the template thus extracted is stored in the data base, during the recognition phase the extracted features are compared with the template in the database. The features of the words are extracted by using LPC analysis. Vector Quantization is used for generating the code books. Finally the recognition decision is made based on the matching score. MATLAB will be used to implement this concept to achieve further understanding. Index Terms: Speech Recognition, LPC, Vector Quantization, and Code Book.
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.
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The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
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1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 11, Issue 6 (May. - Jun. 2013), PP 46-50
www.iosrjournals.org
www.iosrjournals.org 46 | Page
Emotion Recognition Based On Audio Speech
Showkat Ahmad Dar1
, Zahid Khaki2
1
Department of Computer Science and Engineering, Islamic University of Science and Technology, Awantipora
2
Department of Electronics and Communication Engineering, Islamic University of Science and Technology
Jammu and Kashmir 192221, India
Abstract: Emotion 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. Although the emotional state does not alter the
linguistic content, it is an important factor in human communication, because it provides feedback information
in many applications as making a machine to recognize emotions from speech is not a new idea.
This paper presents automatic text independent speaker emotion recognition system using the pattern
classification methods such as the support vector mechanics (SVM) .Acoustic features are derived from the
speech signal at the segmental level. The segmental features are the features extracted from short frames (10-30
ms) of the speech. Acoustic features are derived from the speech signal at the segmental level. Acoustic features
are represented by Mel frequency cepstral coefficients .A 39 dimensional MFCC for each frame is used as
acoustic feature vector. The DFT based cepstral coeffiecients are computed by computing IDFT (inverse DFT)
of the log magnitude short time spectrum of speech signal. Mel wraped cepstrum is obtained by inserting an
intermediate step of transforming the frequencies before computing the IDFT. The Mel scale is based on human
perception of frequency of sound. SVMs are used to construct the optimal separating hyper plane for speech
features .SVMs are used to build the models for each speaker and to compare with the test speaker’s feature
vectors.
Keywords: Inverse discrete Fourier transforms(IDFT), linear prediction coefficients(LPC), linear prediction
cepstral coefficients(LPCC), Support vector mechanics(SVM), Artificial Neural Networks(ANN), Hidden
Markov Model(HMM), Gaussian Mixture Model(GMM).
I. Introduction
Emotion form a significant part of human interaction, and providing computers with the ability to
recognize and make use of such of such non-verbal information could open the way to new and exciting
paradigms in human- computer interaction. The human face is a key element of non-verbal communication, with
our facial emotion acting as a type of social semaphore.
Without the ability to express and recognize emotions , social interaction is less fulfilling and
stimulating , with either or both parties often unable to fully understand the meaning of the other.
This paper deals with the text-independent Speaker emotion recognition using pattern classification
techniques such as SVM (support vector machine).There are two major models for emotion recognition:
Discriminative model and generative model. Discriminative model consists of Artificial Neural Networks
(ANN) and Support Vector Machines (SVM), etc. Generative model consists of Hidden Markov Model (HMM)
and Gaussian Mixture Model (GMM), etc.Each of them can construct speaker models for speaker recognition
task.
II. Speaker Verification And Identification
The speaker recognition system can be operated in either identification mode or verification mode. In
speaker identification, the goal is to identify the speaker of utterances from a given population where as speaker
verification involves validating the identity claim by a person. Speaker recognition systems can be classified
into text dependent and text independent systems. Text dependent systems require the recitation of a
predominant text, where as text independent systems accept speech utterances of unrestricted text.
1. Speaker Emotion Verification
1.1 Speaker identity exits in the physiological and behavioral characteristics of a speaker. The
physiolocal characteristics correspond to the characteristics of the vocal tract system and that of the voice source
.The behavioral characteristics are due to the manner in which speaker have learnt to use their speech production
apparatus.
1.2 Speaker verification use the system or source features for capturing speaker specific information.
Some of the system features used for speaker verification task are formats, linear prediction coefficients (LPC)
2. Emotion Recognition Based On Audio Speech
www.iosrjournals.org 47 | Page
and linear prediction cepstral coefficients (LPCC). Source features such as Pitch, Intonation, and the linear
prediction residual signal information for speaker verification.
III. Linear Prediction Coefficients
The theory of liner prediction (LP) is closely linked to modeling of the vocal tract system, and relies up
on the fact that a particular speech sample may be predicted by a linear weight sum of the previous samples. The
number of previous samples used for prediction is known as the order of prediction. The weights applied to each
of the previous speech sample are known as linear prediction coefficients (LPC).They is calculated so as to
minimize the prediction error.
A given speech sample at time n, s (n), can be approximated as a linear combination of the past p speech
samples, such that
S (n) ≈ a1s (n − 1) + a2s (n − 2) + a3s (n − 3) + · · · + aps(n − p)
3.1 Preprocessing
To extract the features from the speech signal, the signal must be preprocessed and divided into
successive windows or analysis frames .so the following steps are performed before extracting the features.
Preemphasis: The higher frequencies of the speech signal are generally weak. As a result there may not be high
frequency energy present to extract features at the upper end of the frequency range.
3.2 Preemphasis
Preemphasis is used to boost the energy of the high frequency signals. The output of the preemphasis, ˆs (n) is
related the input s (n) by the difference equation
ˆs (n) = s (n) −αs (n − 1)
The typical value for α is 0.95
3.3 Frame blocking:
Speech analysis usually assumes that the signal properties change relatively slowly with time. This
allows examination of a short time window of speech to extract parameters presumed to remain fixed for the
duration of the window. Thus to model dynamic parameters, we must divide the signal into successive windows
or analysis frames, so that the parameters can be calculated often enough to follow the relevant changes. The
preemphasized speech signal, ˆs (n) is blocked into frames of N samples (frame size), with adjacent frames
being separated by M samples (frame shift). If we denote the ltframe of speech by xl (n), and there are L frames
within the entire speech signal then
xl (n) = ˆs(Ml + n), 0 ≤ n ≤ N − 1, 0 ≤ l ≤ N – 1
3.4 Windowing: The next step in the processing is to window each individual Frame so as to minimize the
signal discontinuities at the beginning and end of the frame. The window must be selected to taper the signal to
zero at the beginning and end of each frame. If we define the window as w (n), 0 ≤ n ≤ N-1, then the result of
windowing the signal is
˜xl(n) = xl(n)w(n), 0 ≤ n ≤ N − 1
IV. Linear Prediction Cepstral Coefficients
In many applications, Euclidean distance is used as a measure of similarity or dissimilarity between
feature vectors. The sharp peak of the LP spectrum may produce large errors in a similarity test, even for a slight
shift in the position of peaks .Hence the linear prediction coefficients are converted into cepstral coefficients
using a recursive relation.Cepstral coefficients represents the long magnitude spectrum,and the first few model
the smooth envelop of log spectrum .These coefficients can be obtained either from linear prediction
coefficients or from inverse discrete fourire transform (IDFT).
3. Emotion Recognition Based On Audio Speech
www.iosrjournals.org 48 | Page
IDFT (log (|DFT(x)|))
If x is LPC, the cepstral coefficients are known as Linear Prediction Cepstral
Coefficients (LPCC).
V. Acoustic Feature Extraction
MFCC are widely used spectral features for speaker recognition , computation of the MFCC defers
from the basic procedures described earlier, where the long magnitude spectrum is replaced with logarithm of
Mel scale warped spectrum, prior to inverse Fourier transform operation .Hence the MFCC can be represented
by the gross characteristics of the vocal tract system .i.e. IDFT (log|DFT(x)|))
5.1 Process visualizaton
Figure 5.1 will serve as the audio signal intended for the analysis in this case. The next step is to frame the audio
sample into portions of a predetermined size Figure 5.2 shows a frame belonging to a digital audio signal
Figure 5.2
Next a window function is applied to the frame, in this case a Hamming window was used on the individual
frames to smooth out the frame edges and reduce spectral distortion.
4. Emotion Recognition Based On Audio Speech
www.iosrjournals.org 49 | Page
Then the spectral coefficients are processed with Mel-scale filter to convert these to the Mel scale. The filter
bank used may be viewed in the Fig 5.3 the logarithms of these Mel spectral coefficients are then transformed to
the frequency domain with the inverse DFT.
Fig 5.3 Audio Frame
Fig 5.4 Audio window
VI. Conclusion
This paper allows for asynchrony in the audio and vocabulary states, while preserving the natural
dependency of the audio signals. The audio sequences are treated separately and are no need for the problem.
The advantage of the audio reorganization is confirmed by the experimental results. This model can be
improved applied to a variety of human/machine system. In future work, we will improve current model to
increase the efficiency, besides this, the research on recognition of emotion intensity will be performed through
the analysis of audio feature s, which is different from the approach in. Also, we notice that some new
dimensional reduction and the pattern classification methods like tensor based analysis proposed recently; we
will carry out study on its application in emotion recognition field.
5. Emotion Recognition Based On Audio Speech
www.iosrjournals.org 50 | Page
Reference
[1]. Pongtep Angkititrakul and John H. L. Hansen, “Discrimination in–Set/out-of-set Speaker Recognition Systems” IEEE
TRANSACTIONS ON AUDIO , SPEECH , AND LANGUAGE PROCESSING, Vol.15, no.2, Feb.2007.
[2]. Ran D. Zilca , Brain Kingsbury, Jiri Navrati, Ganesh N. Ramasamy “PSEUDO PITCH SYNCHRONOUS ANALYSIS OF SPEECH
WITH APPLICATION TO SPEAKER RECOGNITION”, IEEE TRANSACTION ON AUDIO , SPEECH, AND LANGUAGE
PROCESSING Vol.14, no. 2, Mar.2006.
[3]. “yildirim, S. Bulut, M., Lee , C.M., kazemzadeh, A., Busso, C., Deng, Z., Lee, S., Narayanan, S., 2004. An Acoustic Study Of
Emotions Expressed In Speech. In: Proc. Internat. Conf. On Spoken Language Processing (ICSLP ’
04), Korea, Vol.
[4]. K. Sri Rama Murty and B. Yengnanarayana “ Combining Evidence from Residual Phase and MFCC Features for Speaker Emotion
Recognition” IEEE signals processing letters, Vol.13,no. , jan.2006
[5]. Volunteers in Technical Assistance (VITA).
[6]. Guillermo Garcia, Sung-kyo Jung, ”A Statistical Approach To Performance Evaluation Of Speaker Emotion Recognition
Systems” TECH/SSTP Lab., France Telecom R&D 22307 Lannion, France. W.B. Mikhel and Pravinkumar Premakanthan, “An
Improved Speaker Emotion Identification Technique Employing Multiple Representations of LPC”, in proceedings of IEEE ,
march 2000
[7]. Sachin S. Kajarekar “Four Weighing And Fusion : A Cepstral –SVM System For Speaker Emotion Recognition VOL. 23, NO. 3,
AUGUST 2008. .