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Dr. Iosif Mporas
School of Engineering and Computer Science, University of Hertfordshire (UH), College Lane Campus, Hatfield AL10 9AB, Hertfordshire, UK

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Research Keywords & Expertise

0 Machine Learning
0 Natural Language Processing
0 Human-machine interaction
0 biosignal processing
0 Speech and audio processing

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Journal article
Published: 27 April 2021 in Energies
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Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

ACS Style

Pascal Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation. Energies 2021, 14, 2485 .

AMA Style

Pascal Schirmer, Iosif Mporas, Akbar Sheikh-Akbari. Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation. Energies. 2021; 14 (9):2485.

Chicago/Turabian Style

Pascal Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. 2021. "Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation." Energies 14, no. 9: 2485.

Journal article
Published: 20 January 2021 in IEEE Access
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Energy storage systems will play a key role in the establishment of future smart grids. Specifically, the integration of storages into the grid architecture serves several purposes, including the handling of the statistical variation of energy supply through increasing usage of renewable energy sources as well as the optimization of the daily energy usage through load scheduling. This article is focusing on the reduction of the grid distortions using non-linear convex optimization. In detail an analytic storage model is used in combination with a load forecasting technique based on socio-economic information of a community of households. It is shown that the proposed load forecasting technique leads to significantly reduced forecasting errors (relative reductions up-to 14.2%), while the proposed storage optimization based on non-linear convex optimizations leads to 12.9% reductions in terms of peak to average values for ideal storages and 9.9% for storages with consideration of losses respectively. Furthermore, it was shown that the largest improvements can be made when storages are utilized for a community of households with a storage size of 4.6-8.2 kWh per household showing the effectiveness of shared storages as well as load forecasting for a community of households.

ACS Style

Pascal A. Schirmer; Christian Geiger; Iosif Mporas. Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages. IEEE Access 2021, 9, 15122 -15132.

AMA Style

Pascal A. Schirmer, Christian Geiger, Iosif Mporas. Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages. IEEE Access. 2021; 9 ():15122-15132.

Chicago/Turabian Style

Pascal A. Schirmer; Christian Geiger; Iosif Mporas. 2021. "Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages." IEEE Access 9, no. : 15122-15132.

Journal article
Published: 04 January 2021 in Neural Computing and Applications
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Energy smart meters have become very popular in monitoring and smart energy management applications. However, the acquired measurements except the energy consumption information may also carry information about the residents’ daily routine, preferences and profile. In this article, we investigate the potential of extracting information from smart meters related to residents’ security- and privacy-sensitive information. Specifically, using methodologies for load demand prediction, non-intrusive load monitoring and elastic matching, evaluation of extraction of information related to house occupancy, multimedia watching detection, socioeconomic and health profiling of residents was performed. The evaluation results showed that the aggregated energy consumption signals contain information related to residents’ privacy and security, which can be extracted from the smart meter measurements.

ACS Style

Pascal Alexander Schirmer; Iosif Mporas. On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters. Neural Computing and Applications 2021, 1 -14.

AMA Style

Pascal Alexander Schirmer, Iosif Mporas. On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters. Neural Computing and Applications. 2021; ():1-14.

Chicago/Turabian Style

Pascal Alexander Schirmer; Iosif Mporas. 2021. "On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters." Neural Computing and Applications , no. : 1-14.

Journal article
Published: 08 December 2020 in Electronics
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This paper presents the research and development of the prototype of the assistive mobile information robot (AMIR). The main features of the presented prototype are voice and gesture-based interfaces with Russian speech and sign language recognition and synthesis techniques and a high degree of robot autonomy. AMIR prototype’s aim is to be used as a robotic cart for shopping in grocery stores and/or supermarkets. Among the main topics covered in this paper are the presentation of the interface (three modalities), the single-handed gesture recognition system (based on a collected database of Russian sign language elements), as well as the technical description of the robotic platform (architecture, navigation algorithm). The use of multimodal interfaces, namely the speech and gesture modalities, make human-robot interaction natural and intuitive, as well as sign language recognition allows hearing-impaired people to use this robotic cart. AMIR prototype has promising perspectives for real usage in supermarkets, both due to its assistive capabilities and its multimodal user interface.

ACS Style

Dmitry Ryumin; Ildar Kagirov; Alexandr Axyonov; Nikita Pavlyuk; Anton Saveliev; Irina Kipyatkova; Milos Zelezny; Iosif Mporas; Alexey Karpov. A Multimodal User Interface for an Assistive Robotic Shopping Cart. Electronics 2020, 9, 2093 .

AMA Style

Dmitry Ryumin, Ildar Kagirov, Alexandr Axyonov, Nikita Pavlyuk, Anton Saveliev, Irina Kipyatkova, Milos Zelezny, Iosif Mporas, Alexey Karpov. A Multimodal User Interface for an Assistive Robotic Shopping Cart. Electronics. 2020; 9 (12):2093.

Chicago/Turabian Style

Dmitry Ryumin; Ildar Kagirov; Alexandr Axyonov; Nikita Pavlyuk; Anton Saveliev; Irina Kipyatkova; Milos Zelezny; Iosif Mporas; Alexey Karpov. 2020. "A Multimodal User Interface for an Assistive Robotic Shopping Cart." Electronics 9, no. 12: 2093.

Review article
Published: 22 October 2020 in Anaesthesia Critical Care & Pain Medicine
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The 2020 International Web Scientific Event in COVID-19 pandemic in critically ill patients aimed at updating the information and knowledge on the COVID-19 pandemic in the intensive care unit. Experts reviewed the latest literature relating to the COVID-19 pandemic in critically ill patients, such as epidemiology, pathophysiology, phenotypes of infection, COVID-19 as a systematic infection, molecular diagnosis, mechanical ventilation, thromboprophylaxis, COVID-19 associated co-infections, immunotherapy, plasma treatment, catheter-related bloodstream infections, artificial intelligence for COVID-19, and vaccination. Antiviral therapy and co-infections are out of the scope of this review. In this review, each of these issues is discussed with key messages regarding management and further research being presented after a brief review of available evidence.

ACS Style

Jordi Rello; Mirko Belliato; Meletios-Athanasios Dimopoulos; Evangelos J. Giamarellos-Bourboulis; Vladimir Jaksic; Ignacio Martin-Loeches; Iosif Mporas; Paolo Pelosi; Garyphallia Poulakou; Spyridon Pournaras; Maximiliano Tamae-Kakazu; Jean-François Timsit; Grant Waterer; Sofia Tejada; George Dimopoulos. Update in COVID-19 in the intensive care unit from the 2020 HELLENIC Athens International symposium. Anaesthesia Critical Care & Pain Medicine 2020, 39, 723 -730.

AMA Style

Jordi Rello, Mirko Belliato, Meletios-Athanasios Dimopoulos, Evangelos J. Giamarellos-Bourboulis, Vladimir Jaksic, Ignacio Martin-Loeches, Iosif Mporas, Paolo Pelosi, Garyphallia Poulakou, Spyridon Pournaras, Maximiliano Tamae-Kakazu, Jean-François Timsit, Grant Waterer, Sofia Tejada, George Dimopoulos. Update in COVID-19 in the intensive care unit from the 2020 HELLENIC Athens International symposium. Anaesthesia Critical Care & Pain Medicine. 2020; 39 (6):723-730.

Chicago/Turabian Style

Jordi Rello; Mirko Belliato; Meletios-Athanasios Dimopoulos; Evangelos J. Giamarellos-Bourboulis; Vladimir Jaksic; Ignacio Martin-Loeches; Iosif Mporas; Paolo Pelosi; Garyphallia Poulakou; Spyridon Pournaras; Maximiliano Tamae-Kakazu; Jean-François Timsit; Grant Waterer; Sofia Tejada; George Dimopoulos. 2020. "Update in COVID-19 in the intensive care unit from the 2020 HELLENIC Athens International symposium." Anaesthesia Critical Care & Pain Medicine 39, no. 6: 723-730.

Journal article
Published: 21 October 2020 in Applied Sciences
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In this article, we present a framework for automatic detection of logging activity in forests using audio recordings. The framework was evaluated in terms of logging detection classification performance and various widely used classification methods and algorithms were tested. Experimental setups, using different ratios of sound-to-noise values, were followed and the best classification accuracy was reported by the support vector machine algorithm. In addition, a postprocessing scheme on decision level was applied that provided an improvement in the performance of more than 1%, mainly in cases of low ratios of sound-to-noise. Finally, we evaluated a late-stage fusion method, combining the postprocessed recognition results of the three top-performing classifiers, and the experimental results showed a further improvement of approximately 2%, in terms of absolute improvement, with logging sound recognition accuracy reaching 94.42% when the ratio of sound-to-noise was equal to 20 dB.

ACS Style

Iosif Mporas; Isidoros Perikos; Vasilios Kelefouras; Michael Paraskevas. Illegal Logging Detection Based on Acoustic Surveillance of Forest. Applied Sciences 2020, 10, 7379 .

AMA Style

Iosif Mporas, Isidoros Perikos, Vasilios Kelefouras, Michael Paraskevas. Illegal Logging Detection Based on Acoustic Surveillance of Forest. Applied Sciences. 2020; 10 (20):7379.

Chicago/Turabian Style

Iosif Mporas; Isidoros Perikos; Vasilios Kelefouras; Michael Paraskevas. 2020. "Illegal Logging Detection Based on Acoustic Surveillance of Forest." Applied Sciences 10, no. 20: 7379.

Journal article
Published: 08 June 2020 in IEEE Access
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Spectroscopic methods in tandem with machine learning methodologies have attracted considerable research interest for the estimation of food quality. The objective of this study was the evaluation of Fourier transform infrared (FTIR) spectroscopy and multispectral imaging (MSI) coupled with appropriate machine learning regression algorithms for assessing meat microbiological quality. For this purpose, minced pork patties were stored aerobically and under modified atmosphere packaging (MAP) conditions, at isothermal and dynamic temperature conditions. At regular time intervals during storage, samples were subjected to (i) microbiological analysis, (ii) FTIR measurements and (iii) MSI acquisition. The collected FTIR data were processed by feature extraction methods to reduce dimensionality, and subsequently Support Vector Machines (SVM) regression models were trained using spectral features (FTIR and MSI) to estimate microbiological quality of meat (microbial population). The regression models were evaluated with different experimental replicates using distinct meat batches. The performance of the models was evaluated in terms of correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The RMSE values for the microbial population estimation models using FTIR were 1.268 and 1.024 for aerobic and MAP storage, respectively. The performance in terms of RMSE for the MSI-based models was 1.144 for aerobic and 0.923 for MAP storage, while the combination of FTIR and MSI spectra resulted in models with RMSE equal to 1.146 for aerobic and 0.886 for MAP storage. The experimental results demonstrated the potential of estimating the microbiological quality of minced pork meat from spectroscopic data.

ACS Style

Lemonia-Christina Fengou; Iosif Mporas; Evgenia Spyrelli; Alexandra Lianou; George-John Nychas. Estimation of the Microbiological Quality of Meat Using Rapid and Non-Invasive Spectroscopic Sensors. IEEE Access 2020, 8, 106614 -106628.

AMA Style

Lemonia-Christina Fengou, Iosif Mporas, Evgenia Spyrelli, Alexandra Lianou, George-John Nychas. Estimation of the Microbiological Quality of Meat Using Rapid and Non-Invasive Spectroscopic Sensors. IEEE Access. 2020; 8 ():106614-106628.

Chicago/Turabian Style

Lemonia-Christina Fengou; Iosif Mporas; Evgenia Spyrelli; Alexandra Lianou; George-John Nychas. 2020. "Estimation of the Microbiological Quality of Meat Using Rapid and Non-Invasive Spectroscopic Sensors." IEEE Access 8, no. : 106614-106628.

Journal article
Published: 12 May 2020 in IEEE Access
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ACS Style

Nikos Fazakis; Georgios Kostopoulos; Sotiris Kotsiantis; Iosif Mporas. Iterative Robust Semi-Supervised Missing Data Imputation. IEEE Access 2020, 8, 90555 -90569.

AMA Style

Nikos Fazakis, Georgios Kostopoulos, Sotiris Kotsiantis, Iosif Mporas. Iterative Robust Semi-Supervised Missing Data Imputation. IEEE Access. 2020; 8 ():90555-90569.

Chicago/Turabian Style

Nikos Fazakis; Georgios Kostopoulos; Sotiris Kotsiantis; Iosif Mporas. 2020. "Iterative Robust Semi-Supervised Missing Data Imputation." IEEE Access 8, no. : 90555-90569.

Journal article
Published: 01 May 2020 in Energies
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A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

ACS Style

Pascal A. Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors. Energies 2020, 13, 2148 .

AMA Style

Pascal A. Schirmer, Iosif Mporas, Akbar Sheikh-Akbari. Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors. Energies. 2020; 13 (9):2148.

Chicago/Turabian Style

Pascal A. Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. 2020. "Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors." Energies 13, no. 9: 2148.

Journal article
Published: 20 April 2020 in Computers
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There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli. Specifically, we investigate the discriminative capacity of the Logarithmic Energy (LogE), Linear Frequency Cepstral Coefficients (LFCC), Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task. We carried out a comparative evaluation with eighteen modifications of the above-mentioned EEG features that cover different frequency bands and use different energy decomposition methods and spectral resolutions. For that purpose, we made use of Naïve Bayes classifier (NB), Classification and regression trees (CART), k-Nearest Neighbors (kNN) classifier, and support vector machines (SVM) classifier with a radial basis function (RBF) kernel trained with the Sequential Minimal Optimization (SMO) method. The experimental evaluation was performed on the well-known and widely used DEAP dataset. A classification accuracy of up to 98.6% was observed for the best performing combination of pre-processing, EEG features and classifier. These results support that the automated detection of like/dislike reactions based on EEG activity is feasible in a personalized setup. This opens opportunities for the incorporation of such functionality in entertainment, healthcare and security applications.

ACS Style

Firgan Feradov; Iosif Mporas; Todor Ganchev. Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals. Computers 2020, 9, 33 .

AMA Style

Firgan Feradov, Iosif Mporas, Todor Ganchev. Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals. Computers. 2020; 9 (2):33.

Chicago/Turabian Style

Firgan Feradov; Iosif Mporas; Todor Ganchev. 2020. "Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals." Computers 9, no. 2: 33.

Chapter
Published: 18 January 2020 in Blockchain Technology and Innovations in Business Processes
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In this paper, we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image preprocessing for natural hair removal and image segmentation for extraction of the skin lesion area followed by computation of statistical values of colors as features. The color-based features were extracted from several well-known and widely used color models. Several classification algorithms were evaluated with the best performing classification algorithm being the AdaBoost with random forest classifier with classification accuracy equal to 73.08% when using RGB-based features only and 74.26% when combining RGB, HSV, and YIQ color model-based features.

ACS Style

Iosif Mporas; Isidoros Perikos; Michael Paraskevas. Color Models for Skin Lesion Classification from Dermatoscopic Images. Blockchain Technology and Innovations in Business Processes 2020, 85 -98.

AMA Style

Iosif Mporas, Isidoros Perikos, Michael Paraskevas. Color Models for Skin Lesion Classification from Dermatoscopic Images. Blockchain Technology and Innovations in Business Processes. 2020; ():85-98.

Chicago/Turabian Style

Iosif Mporas; Isidoros Perikos; Michael Paraskevas. 2020. "Color Models for Skin Lesion Classification from Dermatoscopic Images." Blockchain Technology and Innovations in Business Processes , no. : 85-98.

Journal article
Published: 06 January 2020 in Entropy
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In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.

ACS Style

Pascal A. Schirmer; Iosif Mporas; Michael Paraskevas. Energy Disaggregation Using Elastic Matching Algorithms. Entropy 2020, 22, 71 .

AMA Style

Pascal A. Schirmer, Iosif Mporas, Michael Paraskevas. Energy Disaggregation Using Elastic Matching Algorithms. Entropy. 2020; 22 (1):71.

Chicago/Turabian Style

Pascal A. Schirmer; Iosif Mporas; Michael Paraskevas. 2020. "Energy Disaggregation Using Elastic Matching Algorithms." Entropy 22, no. 1: 71.

Journal article
Published: 11 June 2019 in Sustainability
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In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.

ACS Style

Pascal Schirmer; Iosif Mporas. Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation. Sustainability 2019, 11, 3222 .

AMA Style

Pascal Schirmer, Iosif Mporas. Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation. Sustainability. 2019; 11 (11):3222.

Chicago/Turabian Style

Pascal Schirmer; Iosif Mporas. 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation." Sustainability 11, no. 11: 3222.

Journal article
Published: 15 May 2019 in Sensors
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Digital camera sensors are designed to record all incident light from a captured scene, but they are unable to distinguish between the colour of the light source and the true colour of objects. The resulting captured image exhibits a colour cast toward the colour of light source. This paper presents a colour constancy algorithm for images of scenes lit by non-uniform light sources. The proposed algorithm uses a histogram-based algorithm to determine the number of colour regions. It then applies the K-means++ algorithm on the input image, dividing the image into its segments. The proposed algorithm computes the Normalized Average Absolute Difference (NAAD) for each segment and uses it as a measure to determine if the segment has sufficient colour variations. The initial colour constancy adjustment factors for each segment with sufficient colour variation is calculated. The Colour Constancy Adjustment Weighting Factors (CCAWF) for each pixel of the image are determined by fusing the CCAWFs of the segments, weighted by their normalized Euclidian distance of the pixel from the center of the segments. Results show that the proposed method outperforms the statistical techniques and its images exhibit significantly higher subjective quality to those of the learning-based methods. In addition, the execution time of the proposed algorithm is comparable to statistical-based techniques and is much lower than those of the state-of-the-art learning-based methods.

ACS Style

Akmol Hussain; Akbar Sheikh-Akbari; Iosif Mporas. Colour Constancy for Image of Non-Uniformly Lit Scenes. Sensors 2019, 19, 2242 .

AMA Style

Akmol Hussain, Akbar Sheikh-Akbari, Iosif Mporas. Colour Constancy for Image of Non-Uniformly Lit Scenes. Sensors. 2019; 19 (10):2242.

Chicago/Turabian Style

Akmol Hussain; Akbar Sheikh-Akbari; Iosif Mporas. 2019. "Colour Constancy for Image of Non-Uniformly Lit Scenes." Sensors 19, no. 10: 2242.

Conference paper
Published: 13 August 2016 in Computer Vision
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In this paper we present a fusion methodology for combining prompted text-dependent and text-independent speaker verification operation modalities. The fusion is performed in score level extracted from GMM-UBM single mode speaker verification engines using several machine learning algorithms for classification. In order to improve the performance we apply clustering of the score-based data before the classification stage. The experimental results indicated that the fusion of the two operation modes improves the speaker verification performance both in terms of sensitivity and specificity by approximately 2 % and 1.5 % respectively.

ACS Style

Iosif Mporas; Saeid Safavi; Reza Sotudeh. Improving Robustness of Speaker Verification by Fusion of Prompted Text-Dependent and Text-Independent Operation Modalities. Computer Vision 2016, 378 -385.

AMA Style

Iosif Mporas, Saeid Safavi, Reza Sotudeh. Improving Robustness of Speaker Verification by Fusion of Prompted Text-Dependent and Text-Independent Operation Modalities. Computer Vision. 2016; ():378-385.

Chicago/Turabian Style

Iosif Mporas; Saeid Safavi; Reza Sotudeh. 2016. "Improving Robustness of Speaker Verification by Fusion of Prompted Text-Dependent and Text-Independent Operation Modalities." Computer Vision , no. : 378-385.

Journal article
Published: 01 June 2016 in Image Processing & Communications
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We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.

ACS Style

Hock Gan; Iosif Mporas; Saeid Safavi; Reza Sotudeh. Speaker Identification Using Data-Driven Score Classification. Image Processing & Communications 2016, 21, 55 -63.

AMA Style

Hock Gan, Iosif Mporas, Saeid Safavi, Reza Sotudeh. Speaker Identification Using Data-Driven Score Classification. Image Processing & Communications. 2016; 21 (2):55-63.

Chicago/Turabian Style

Hock Gan; Iosif Mporas; Saeid Safavi; Reza Sotudeh. 2016. "Speaker Identification Using Data-Driven Score Classification." Image Processing & Communications 21, no. 2: 55-63.