Search results for: speech emotion recognition

Authors: Ehab Mourtaga, Ahmad Sharieh, Mousa Abdallah

Abstract:

An automatic speech recognition system for the formal Arabic language is needed. The Quran is the most formal spoken book in Arabic, it is spoken all over the world. In this research, an automatic speech recognizer for Quranic based speakerindependent was developed and tested. The system was developed based on the tri-phone Hidden Markov Model and Maximum Likelihood Linear Regression (MLLR). The MLLR computes a set of transformations which reduces the mismatch between an initial model set and the adaptation data. It uses the regression class tree, as well as, estimates a set of linear transformations for the mean and variance parameters of a Gaussian mixture HMM system. The 30th Chapter of the Quran, with five of the most famous readers of the Quran, was used for the training and testing of the data. The chapter includes about 2000 distinct words. The advantages of using the Quranic verses as the database in this developed recognizer are the uniqueness of the words and the high level of orderliness between verses. The level of accuracy from the tested data ranged 68 to 85%.

Keywords: Hidden Markov Model (HMM), MaximumLikelihood Linear Regression (MLLR), Quran, Regression ClassTree, Speech Recognition, Speaker-independent.

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Authors: Jia-Ru Li, Chih-Hung Wang, Ching-Wen Lin

Abstract:

The association between emotional inhibition strategies linked to depression has been showed inconsistent among studies. Mild emotional inhibition maybe benefit for social interaction, especially for female among East Asian cultures. The present study aimed to examine whether the inhibition–depression relationship is dependent on level of emotion inhibition and gender context, given differing value of suppressing emotional displays. We hypothesized that the negative associations between inhibition and adolescent depression would not directly, in which affected by interaction between emotion inhibition and gender. To test this hypothesis, we asked 309 junior high school students which age range from 12 to14 years old to report on their use of emotion inhibition and depression syndrome. A multiple regressions analysis revealed that significant interaction that gender as a moderator to the relationships between emotion inhibition and adolescent depression. The group with the highest level of depression was girls with high levels of emotion inhibition, whose depression score was higher than that of boys with high levels of emotion inhibition. The result highlights that the importance of context in understanding the inhibition-depression relationship.

Keywords: Emotional inhibition strategies, gender, adolescent depression.

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Authors: H. B. Kekre, Tanuja K. Sarode

Abstract:

Mostly transforms are used for speech data compressions which are lossy algorithms. Such algorithms are tolerable for speech data compression since the loss in quality is not perceived by the human ear. However the vector quantization (VQ) has a potential to give more data compression maintaining the same quality. In this paper we propose speech data compression algorithm using vector quantization technique. We have used VQ algorithms LBG, KPE and FCG. The results table shows computational complexity of these three algorithms. Here we have introduced a new performance parameter Average Fractional Change in Speech Sample (AFCSS). Our FCG algorithm gives far better performance considering mean absolute error, AFCSS and complexity as compared to others.

Keywords: Vector Quantization, Data Compression, Encoding, , Speech coding.

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Authors: E. Krasnova, E. Bulgakova, V. Shchemelinin

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The paper deals with acoustic-spectrographic voice identification method in terms of its performance in non-native language speech. Performance evaluation is conducted by comparing the result of the analysis of recordings containing native language speech with recordings that contain foreign language speech. Our research is based on Tajik and Russian speech of Tajik native speakers due to the character of the criminal situation with drug trafficking. We propose a pilot experiment that represents a primary attempt enter the field.

Keywords: Speaker identification, acoustic-spectrographic method, non-native speech.

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Authors: Nazaket Gazieva

Abstract:

Voice biometric data associated with physiological, psychological and other factors are widely used in forensic phonoscopy. There are various methods for identifying and verifying a person by voice. This article explores the minimum speech signal data as individual parameters of a speech signal. Monozygotic twins are believed to be genetically identical. Using the minimum data of the speech signal, we came to the conclusion that the voice imprint of monozygotic twins is individual. According to the conclusion of the experiment, we can conclude that the minimum indicators of the speech signal are more stable and reliable for phonoscopic examinations.

Keywords: Biometric voice prints, fundamental frequency, phonogram, speech signal, temporal characteristics.

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Authors: Syed Akhter Hossain, M. Lutfar Rahman, Farruk Ahmed

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Bangla Vowel characterization determines the spectral properties of Bangla vowels for efficient synthesis as well as recognition of Bangla vowels. In this paper, Bangla vowels in isolated word have been analyzed based on speech production model within the framework of Analysis-by-Synthesis. This has led to the extraction of spectral parameters for the production model in order to produce different Bangla vowel sounds. The real and synthetic spectra are compared and a weighted square error has been computed along with the error in the formant bandwidths for efficient representation of Bangla vowels. The extracted features produced good representation of targeted Bangla vowel. Such a representation also plays essential role in low bit rate speech coding and vocoders.

Keywords: Speech, vowel, formant, synthesis, spectrum, LPC.

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Authors: Kei Fujii, Jun Okawa, Kaori Suigetsu

Abstract:

Concatenative speech synthesis is a method that can make speech sound which has naturalness and high-individuality of a speaker by introducing a large speech corpus. Based on this method, in this paper, we propose a voice conversion method whose conversion speech has high-individuality and naturalness. The authors also have two subjective evaluation experiments for evaluating individuality and sound quality of conversion speech. From the results, following three facts have be confirmed: (a) the proposal method can convert the individuality of speakers well, (b) employing the framework of unit selection (especially join cost) of concatenative speech synthesis into conventional voice conversion improves the sound quality of conversion speech, and (c) the proposal method is robust against the difference of genders between a source speaker and a target speaker.

Keywords: concatenative speech synthesis, join cost, speaker individuality, unit selection, voice conversion

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Authors: A. R. Bagirzade, A. Sh. Najafova, S. M. Yessirkepova, E. S. Albert

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This article describes a text recognition method based on Optical Character Recognition (OCR). The features of the OCR method were examined using the ABBYY FineReader program. It describes automatic text recognition in images. OCR is necessary because optical input devices can only transmit raster graphics as a result. Text recognition describes the task of recognizing letters shown as such, to identify and assign them an assigned numerical value in accordance with the usual text encoding (ASCII, Unicode). The peculiarity of this study conducted by the authors using the example of the ABBYY FineReader, was confirmed and shown in practice, the improvement of digital text recognition platforms developed by Electronic Publication.

Keywords: ABBYY FineReader system, algorithm symbol recognition, OCR/ICR techniques, recognition technologies.

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Authors: V. Kabeer, N.K.Narayanan

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This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.

Keywords: Face recognition, Image analysis, Wavelet feature extraction, Pattern recognition, Classifier algorithms

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Authors: Hirohide Ushida

Abstract:

This paper discusses an artificial mind model and its applications. The mind model is based on some theories which assert that emotion is an important function in human decision making. An artificial mind model with emotion is built, and the model is applied to action selection of autonomous agents. In three examples, the agents interact with humans and their environments. The examples show the proposed model effectively work in both virtual agents and real robots.

Keywords: Artificial mind, emotion, interactive agent, pet robot

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Authors: Mayank Kumar, D B Mahesh Kumar, Ashwin S Kumar, N K Srinath

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In this era of technology, fueled by the pervasive usage of the internet, security is a prime concern. The number of new attacks by the so-called “bots", which are automated programs, is increasing at an alarming rate. They are most likely to attack online registration systems. Technology, called “CAPTCHA" (Completely Automated Public Turing test to tell Computers and Humans Apart) do exist, which can differentiate between automated programs and humans and prevent replay attacks. Traditionally CAPTCHA-s have been implemented with the challenge involved in recognizing textual images and reproducing the same. We propose an approach where the visual challenge has to be read out from which randomly selected keywords are used to verify the correctness of spoken text and in turn detect the presence of human. This is supplemented with a speaker recognition system which can identify the speaker also. Thus, this framework fulfills both the objectives – it can determine whether the user is a human or not and if it is a human, it can verify its identity.

Keywords: CAPTCHA, automatic speech recognition, keyword spotting.

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Authors: Qianhua He, Weili Zhou, Aiwu Chen

Abstract:

A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.

Keywords: Speech denoising, sparse representation, K-singular value decomposition, orthogonal matching pursuit.

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Authors: B. Kuiper

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When Dwight D. Eisenhower delivered his final Presidential speech in 1961, he was using the opportunity to bid farewell to America, but he was also trying to warn his fellow countrymen about deeper challenges threatening the country. In this analysis, Eisenhower’s speech is examined in light of the impact it had on American culture, communication concepts, and political ramifications. The paper initially highlights the previous literature on the speech, especially in light of its 50^th anniversary, and reveals a man whose main concern was how the speech’s words would affect his beloved country. The painstaking approach to the wording of the speech to reveal the intent is key, particularly in light of analyzing the motivations according to “virtuous communication.” This philosophical construct indicates that Eisenhower’s Farewell Address was crafted carefully according to a departing President’s deepest values and concerns, concepts that he wanted to pass along to his successor, to his country, and even to the world.

Keywords: Eisenhower, mass communication, political speech, rhetoric.

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Authors: Talbi Mourad, Salhi Lotfi, Chérif Adnen

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In this work, we are interested in developing a speech denoising tool by using a discrete wavelet packet transform (DWPT). This speech denoising tool will be employed for applications of recognition, coding and synthesis. For noise reduction, instead of applying the classical thresholding technique, some wavelet packet nodes are set to zero and the others are thresholded. To estimate the non stationary noise level, we employ the spectral entropy. A comparison of our proposed technique to classical denoising methods based on thresholding and spectral subtraction is made in order to evaluate our approach. The experimental implementation uses speech signals corrupted by two sorts of noise, white and Volvo noises. The obtained results from listening tests show that our proposed technique is better than spectral subtraction. The obtained results from SNR computation show the superiority of our technique when compared to the classical thresholding method using the modified hard thresholding function based on u-law algorithm.

Keywords: Enhancement, spectral subtraction, SNR, discrete wavelet packet transform, spectral entropy Histogram

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Authors: V. Anil Kumar, Abhijit Mitra, S. R. Mahadeva Prasanna

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We analyze the effectivity of different pseudo noise (PN) and orthogonal sequences for encrypting speech signals in terms of perceptual intelligence. Speech signal can be viewed as sequence of correlated samples and each sample as sequence of bits. The residual intelligibility of the speech signal can be reduced by removing the correlation among the speech samples. PN sequences have random like properties that help in reducing the correlation among speech samples. The mean square aperiodic auto-correlation (MSAAC) and the mean square aperiodic cross-correlation (MSACC) measures are used to test the randomness of the PN sequences. Results of the investigation show the effectivity of large Kasami sequences for this purpose among many PN sequences.

Keywords: Speech encryption, pseudo-noise codes, maximallength, Gold, Barker, Kasami, Walsh-Hadamard, autocorrelation, crosscorrelation, figure of merit.

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Authors: Sid-Ahmed. Selouani, Tang-Ho Lê, Chadia Moghrabi, Benoit Lanteigne, Jean Roy

Abstract:

A Web-based learning tool, the Learn IN Context (LINC) system, designed and being used in some institution-s courses in mixed-mode learning, is presented in this paper. This mode combines face-to-face and distance approaches to education. LINC can achieve both collaborative and competitive learning. In order to provide both learners and tutors with a more natural way to interact with e-learning applications, a conversational interface has been included in LINC. Hence, the components and essential features of LINC+, the voice enhanced version of LINC, are described. We report evaluation experiments of LINC/LINC+ in a real use context of a computer programming course taught at the Université de Moncton (Canada). The findings show that when the learning material is delivered in the form of a collaborative and voice-enabled presentation, the majority of learners seem to be satisfied with this new media, and confirm that it does not negatively affect their cognitive load.

Keywords: E-leaning, Knowledge Network, Speech recognition, Speech synthesis.

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Authors: Natalia Constantinescu

Abstract:

This cross-sectional study aims to explore the differences among adults with somatic symptom disorder (SSD) versus adults without SSD, in terms of attachment and emotion regulation strategies. A total sample of 80 participants (40 people with SSD and 40 healthy controls), aged 20-57 years old (M = 31.69, SD = 10.55), were recruited from institutions and online groups. They completed the Romanian version of the Experiences in Close Relationships Scale – Short Form (ECR-S), Regulation of Emotion Systems Survey (RESS), Patient Health Questionnaire-15 (PHQ-15) and Somatic Symptom Disorder – B Criteria Scale (SSD-12). The results indicate significant differences between the two groups in terms of attachment and emotion regulation strategies. Adults with SSD have a higher level of attachment anxiety and avoidance compared to the nonclinical group. Moreover, people with SSD are more prone to use rumination and suppression and less prone to use reevaluation compared to healthy people. Implications for SSD prevention and treatment are discussed.

Keywords: adult attachment, emotion regulation strategies, psychosomatic disorders, somatic symptom disorder

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Authors: Justin Leo Cheang Loong, Khazaimatol S Subari, Muhammad Kamil Abdullah, Nurul Nadia Ahmad, RosliBesar

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Heart sound is an acoustic signal and many techniques used nowadays for human recognition tasks borrow speech recognition techniques. One popular choice for feature extraction of accoustic signals is the Mel Frequency Cepstral Coefficients (MFCC) which maps the signal onto a non-linear Mel-Scale that mimics the human hearing. However the Mel-Scale is almost linear in the frequency region of heart sounds and thus should produce similar results with the standard cepstral coefficients (CC). In this paper, MFCC is investigated to see if it produces superior results for PCG based human identification system compared to CC. Results show that the MFCC system is still superior to CC despite linear filter-banks in the lower frequency range, giving up to 95% correct recognition rate for MFCC and 90% for CC. Further experiments show that the high recognition rate is due to the implementation of filter-banks and not from Mel-Scaling.

Keywords: Biometric, Phonocardiogram, Cepstral Coefficients, Mel Frequency

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Authors: Hamid Reza Boveiri

Abstract:

In this paper, a new proposed system for Persian printed numeral characters recognition with emphasis on representation and recognition stages is introduced. For the first time, in Persian optical character recognition, geometrical central moments as character image descriptor and fuzzy min-max neural network for Persian numeral character recognition has been used. Set of different experiments on binary images of regular, translated, rotated and scaled Persian numeral characters has been done and variety of results has been presented. The best result was 99.16% correct recognition demonstrating geometrical central moments and fuzzy min-max neural network are adequate for Persian printed numeral character recognition.

Keywords: Fuzzy min-max neural network, geometrical centralmoments, optical character recognition, Persian digits recognition, Persian printed numeral characters recognition.

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Authors: Tetyana Baydyk, Ernst Kussul, Sandra Bonilla Meza

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There are many articles that attempt to establish the role of different facial fragments in face recognition. Various approaches are used to estimate this role. Frequently, authors calculate the entropy corresponding to the fragment. This approach can only give approximate estimation. In this paper, we propose to use a more direct measure of the importance of different fragments for face recognition. We propose to select a recognition method and a face database and experimentally investigate the recognition rate using different fragments of faces. We present two such experiments in the paper. We selected the PCNC neural classifier as a method for face recognition and parts of the LFW (Labeled Faces in the Wild) face database as training and testing sets. The recognition rate of the best experiment is comparable with the recognition rate obtained using the whole face.

Keywords: Face recognition, Labeled Faces in the Wild (LFW) database, Random Local Descriptor (RLD), random features.

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Authors: Jing Wu, Wei Lv, Yibing Li, Yuanfan You

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The separation of speech signals has become a research hotspot in the field of signal processing in recent years. It has many applications and influences in teleconferencing, hearing aids, speech recognition of machines and so on. The sounds received are usually noisy. The issue of identifying the sounds of interest and obtaining clear sounds in such an environment becomes a problem worth exploring, that is, the problem of blind source separation. This paper focuses on the under-determined blind source separation (UBSS). Sparse component analysis is generally used for the problem of under-determined blind source separation. The method is mainly divided into two parts. Firstly, the clustering algorithm is used to estimate the mixing matrix according to the observed signals. Then the signal is separated based on the known mixing matrix. In this paper, the problem of mixing matrix estimation is studied. This paper proposes an improved algorithm to estimate the mixing matrix for speech signals in the UBSS model. The traditional potential algorithm is not accurate for the mixing matrix estimation, especially for low signal-to noise ratio (SNR).In response to this problem, this paper considers the idea of an improved potential function method to estimate the mixing matrix. The algorithm not only avoids the inuence of insufficient prior information in traditional clustering algorithm, but also improves the estimation accuracy of mixing matrix. This paper takes the mixing of four speech signals into two channels as an example. The results of simulations show that the approach in this paper not only improves the accuracy of estimation, but also applies to any mixing matrix.

Keywords: Clustering algorithm, potential function, speech signal, the UBSS model.

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Authors: Anastasis Kounoudes, Stephanos Mavromoustakos

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Electronic commerce is growing rapidly with on-line sales already heading for hundreds of billion dollars per year. Due to the huge amount of money transferred everyday, an increased security level is required. In this work we present the architecture of an intelligent speaker verification system, which is able to accurately verify the registered users of an e-commerce service using only their voices as an input. According to the proposed architecture, a transaction-based e-commerce application should be complemented by a biometric server where customer-s unique set of speech models (voiceprint) is stored. The verification procedure requests from the user to pronounce a personalized sequence of digits and after capturing speech and extracting voice features at the client side are sent back to the biometric server. The biometric server uses pattern recognition to decide whether the received features match the stored voiceprint of the customer who claims to be, and accordingly grants verification. The proposed architecture can provide e-commerce applications with a higher degree of certainty regarding the identity of a customer, and prevent impostors to execute fraudulent transactions.

Keywords: Speaker Recognition, Biometrics, E-commercesecurity.

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Authors: Tinatin Kurdghelashvili

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The paper deals with the usage of speech acts and politeness strategies in an EFL classroom in Georgia (Rep of). It explores the students’ and the teachers’ practice of the politeness strategies and the speech acts of apology, thanking, request, compliment / encouragement, command, agreeing / disagreeing, addressing and code switching. The research method includes observation as well as a questionnaire. The target group involves the students from Georgian public schools and two certified, experienced local English teachers. The analysis is based on Searle’s Speech Act Theory and Brown and Levinson’s politeness strategies. The findings show that the students have certain knowledge regarding politeness yet they fail to apply them in English communication. In addition, most of the speech acts from the classroom interaction are used by the teachers and not the students. Thereby, it is suggested that teachers should cultivate the students’ communicative competence and attempt to give them opportunities to practise more English speech acts than they do today.

Keywords: English as a foreign language, Georgia, politeness principles, speech acts.

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Authors: Z. Qi, T. J. Moir

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A combined three-microphone voice activity detector (VAD) and noise-canceling system is studied to enhance speech recognition in an automobile environment. A previous experiment clearly shows the ability of the composite system to cancel a single noise source outside of a defined zone. This paper investigates the performance of the composite system when there are frequently moving noise sources (noise sources are coming from different locations but are not always presented at the same time) e.g. there is other passenger speech or speech from a radio when a desired speech is presented. To work in a frequently moving noise sources environment, whilst a three-microphone voice activity detector (VAD) detects voice from a “VAD valid zone", the 3-microphone noise canceller uses a “noise canceller valid zone" defined in freespace around the users head. Therefore, a desired voice should be in the intersection of the noise canceller valid zone and VAD valid zone. Thus all noise is suppressed outside this intersection of area. Experiments are shown for a real environment e.g. all results were recorded in a car by omni-directional electret condenser microphones.

Keywords: Signal processing, voice activity detection, noise canceller, microphone array beam forming.

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Authors: Long Jye Sheu, Horng-Shing Chiou, Wei Ching Chen

Abstract:

We propose a new perspective on speech communication using blind source separation. The original speech is mixed with key signals which consist of the mixing matrix, chaotic signals and a random noise. However, parts of the keys (the mixing matrix and the random noise) are not necessary in decryption. In practice implement, one can encrypt the speech by changing the noise signal every time. Hence, the present scheme obtains the advantages of a One Time Pad encryption while avoiding its drawbacks in key exchange. It is demonstrated that the proposed scheme is immune against traditional attacks.

Keywords: one time pad, blind source separation, independentcomponent analysis, speech encryption.

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Authors: Jia-Ru Li, Chia-Jung Li, Ching-Wen Lin

Abstract:

Contrary to negative emotion regulation, coping with positive moods have received less attention in adolescent adjustment. However, some research has found that everyone is different on dealing with their positive emotions, which affects their adaptation and well-being. The purpose of the present study was to investigate the relationship between positive emotions dampening and internalizing behavior problems of adolescent in Taiwan. A survey was conducted and 208 students (12 to14 years old) completed the strengths and difficulties questionnaire (SDQ), the Affect Intensity Measure, and the positive emotions dampening scale. Analysis methods such as descriptive statistics, t-test, Pearson correlations and multiple regression were adapted. The results were as follows: Emotionality and internalizing problem behavior have significant gender differences. Compared to boys, girls have a higher score on negative emotionality and are at a higher risk for internalizing symptoms. However, there are no gender differences on positive emotion dampening. Additionally, in the circumstance that negative emotionality acted as the control variable, positive emotion dampening strategy was (positive) related to internalizing behavior problems. Given the results of this study, it is suggested that coaching deconstructive positive emotion strategies is to assist adolescents with internalizing behavior problems is encouraged.

Keywords: Emotion dampening strategies, internalizing problem behaviors, affect intensity.

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Authors: M. Kalamani, S. Valarmathy, M. Krishnamoorthi

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In this paper, Least Mean Square (LMS) adaptive noise reduction algorithm is proposed to enhance the speech signal from the noisy speech. In this, the speech signal is enhanced by varying the step size as the function of the input signal. Objective and subjective measures are made under various noises for the proposed and existing algorithms. From the experimental results, it is seen that the proposed LMS adaptive noise reduction algorithm reduces Mean square Error (MSE) and Log Spectral Distance (LSD) as compared to that of the earlier methods under various noise conditions with different input SNR levels. In addition, the proposed algorithm increases the Peak Signal to Noise Ratio (PSNR) and Segmental SNR improvement (ΔSNRseg) values; improves the Mean Opinion Score (MOS) as compared to that of the various existing LMS adaptive noise reduction algorithms. From these experimental results, it is observed that the proposed LMS adaptive noise reduction algorithm reduces the speech distortion and residual noise as compared to that of the existing methods.

Keywords: LMS, speech enhancement, speech quality, residual noise.

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Authors: Samba Raju, Chiluveru, Manoj Tripathy

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Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methods

Keywords: Discrete Wavelet Transform, speech intelligibility, STOI, standard deviation.

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: Virtual Reality, effective computing, effective VR, emotion-based effective physiological database.

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Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay

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Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.

Keywords: Annotated Facial Expression Dataset, Sign Language Recognition, Gesture Recognition, Sequenced Facial Expression Dataset.

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