Speaker tracking system using speaker boundary detection

This thesis is about a research conducted in the area of Speaker Recognition. The application is concerned to the automatic detection and tracking of target speakers in meetings, conferences, telephone conversations and in radio and television broadcasts. A Speaker Tracking system is developed here, in collaboration with the Center for Language and Speech Technologies and Applications (TALP) in UPC. The main objective of this Speaker Tracking system is to answer the question: When the target speaker speaks? The system uses training speech data for the target speaker in the pre-enrollment stage. Three main modules have been designed for this Speaker Tracking system. In the first module an energy based Speech Activity Detection is applied to select the speech parts of the audio. In the second module the audio is segmented according to the speaker turning points. In the last module a Speaker Verification is implemented in which the target speakers are verified and tracked. Two different approaches are applied in this last module. In the first approach for Speaker Verification, the target speakers and the segments are modeled using the state-of-the-art, Gaussian Mixture Models (GMM). In the second approach for Speaker Verification, the identity vectors (i-vectors) representation is applied for the target speakers and the segments. Finally, the performance of both these approaches is compared for the results evaluation

An Islamic banking perspective on consumers’ perception in Pakistan

Purpose – The aim of this research is to examine the perceptions of consumers on Islamic banking and finance in Pakistan. Islamic finance is an emerging phenomenon and its survival depends upon the availability, affordability and awareness. This thesis attempts to fill the gap in the literature by exploring the perceptions of consumers and bankers in an attempt to gain insights so that availability of products and awareness can be increased. Design/methodology/approach – The study uses a regression model by employing perception as a dependent variable and awareness, knowledge and religious motivation as independent variables. The primary data is collected using 150 questionnaires distributed amongst finance students in several universities and employees of Islamic banks in Khyber Pakhtunkhwa (KPK) province of Pakistan. Findings – The findings reveal that overall consumers’ perception is positive about Islamic banking and finance in Pakistan. Statistical analysis shows that awareness, knowledge and religiosity level have positive influence on the perception of consumers about Islamic financing products and services in Pakistan. To improve the awareness and understanding, Islamic banks could make better marketing strategies and could increases their presence by mosque visits and conferences. A cooperation between the industry and scholars could help in more innovative products for the consumers. Implications – There has been a limited amount of work carried out on the perceptions of consumers about Islamic banking in Pakistan. The present study represents start of a larger context for examining Islamic banking practices in Pakistan. The findings of the study can be used as a reference in future research projects in the areas of perceptions and awareness. Originality/value – Little research has been conducted to study this problem from the perspectives of consumers and Islamic banking employees. Most research associated with Islamic banks fails to pay attention to these stakeholder groups in one study

Impact of public debt on growth in Belt & Road countries –Pre and Post Analysis of Financial Crisis 2007-2008

The study seeks to investigate the role of external debt on the growth performance of Belt & Road countries for the pre & post financial crisis period. Using panel data methodologies like fixed effect model and GMM, the study finds a significant negative relationship between external debt and economic growth. Similarly, various specifications are estimated for robustness check like dividing the period into sub-periods, dividing the countries according to continent basis, and applying the generalized method of moment’s techniques. The robustness checks confirm the negative relationship between debt and economic growth. Keywords: External debt, BRI, growth JEL Classification: C33; H63; O43 DOI: 10.7176/RJFA/10-18-18 Publication date:September 30th 201

GLOBALIZAÇÃO E A MUDANÇA DE CONCEITO DA OTAN

North Atlantic Treaty Organization (NATO) has been the most important and successful multilateral military cum political organization, pursuing the agenda of exporting democracy globally and ensuring the mutual defense of its allies. Historically, NATO was formed against the threat of communism emanating from USSR (Russia). The alliance did not use military option till the end of the cold war between the west and USSR, but post-cold war, it transformed and operated in Balkans, South Asia, Horn of Africa, and Middle East. The 9/11 incident further enhanced the military role of the organization and gave it ample reason to act internationally for ensuring the global security. America, being the leader of the alliance used it for fighting the so-called global war on terrorism and its adventures in Middle East. Nevertheless, in the last two decades the organization went through various changes and is now continuously in the state of transformation. The wave of populism which had influenced the very concept of globalization has posed serious challenges for the alliance. The Trump rhetoric of “America first”, BREXIT, challenges of migration, changing demography of Europe, assertion of Russia in global politics, confrontation between the NATO allies like Turkey and France, and rise of China are few factors which may affect the future of the so-called intergovernmental military alliance. This article concurrently discusses the new challenges for the NATO and sheds light on the possible options to the strategy of Biden administration to reverse the policies of its predecessor which have influenced the cooperation of different allies of NATO. In the end researcher has tried to put forth few recommendations which may help the policy makers to cope with the challenges NATO is facing. The study is qualitative and analytical in nature whereas primary as well as secondary sources are used for data collection.Organização do Tratado do Atlântico Norte (OTAN) tem sido a organização militar e política multilateral mais importante e bem-sucedida, perseguindo a agenda de exportação da democracia globalmente e garantindo a defesa mútua de seus aliados. Historicamente, a OTAN foi formada contra a ameaça do comunismo proveniente da URSS (Rússia). A aliança não usou opção militar até o fim da guerra fria entre o Ocidente e a URSS, mas após a guerra fria, ela se transformou e operou nos Bálcãs, Sul da Ásia, Chifre da África e Oriente Médio. O incidente de 11 de setembro reforçou ainda mais o papel militar da organização e deu-lhe uma ampla razão para agir internacionalmente para garantir a segurança global. Estados Unidos, sendo o líder da aliança, usou-a para travar a chamada Guerra Global Contra o Terrorismo e suas aventuras no Oriente Médio. No entanto, nas últimas duas décadas a organização passou por várias mudanças, estando em processo contínuo de transformação. A onda de populismo que influenciou o próprio conceito de globalização colocou sérios desafios para a aliança. A retórica de Trump de “América primeiro”, BREXIT, desafios da migração, mudança demográfica da Europa, afirmação da Rússia na política global, confronto entre os aliados da OTAN como Turquia e França e ascensão da China são alguns fatores que podem afetar o futuro da aliança militar intergovernamental. Este artigo discute concomitantemente os novos desafios para a OTAN e lança luz sobre as opções possíveis para a estratégia da administração Biden para reverter as políticas de seu antecessor que influenciaram a cooperação de diferentes aliados da OTAN. No final, os pesquisadores apresentam algumas recomendações que possam ajudar os tomadores de decisão a lidar com os desafios que a OTAN está enfrentando. O estudo é de natureza qualitativa e analítica, ao passo que fontes primárias e secundárias são utilizadas para a coleta de dados

A multiprocessing platform for transient event detection

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references.by Umair A. Khan.M.S

The UPC speaker verification system submitted to VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20)

This report describes the submission from Technical University of Catalonia (UPC) to the VoxCeleb Speaker Recognition Challenge (VoxSRC-20) at Interspeech 2020. The final submission is a combination of three systems. System-1 is an autoencoder based approach which tries to reconstruct similar i-vectors, whereas System-2 and -3 are Convolutional Neural Network (CNN) based siamese architectures. The siamese networks have two and three branches, respectively, where each branch is a CNN encoder. The double-branch siamese performs binary classification using cross entropy loss during training. Whereas, our triple-branch siamese is trained to learn speaker embeddings using triplet loss. We provide results of our systems on VoxCeleb-1 test, VoxSRC-20 validation and test sets.This report describes the submission from Technical University of Catalonia (UPC) to the VoxCeleb Speaker Recognition Challenge (VoxSRC-20) at Interspeech 2020. The final submission is a combination of three systems. System-1 is an autoencoder based approach which tries to reconstruct similar i-vectors, whereas System-2 and -3 are Convolutional Neural Network (CNN) based siamese architectures. The siamese networks have two and three branches, respectively, where each branch is a CNN encoder. The double-branch siamese performs binary classification using cross entropy loss during training. Whereas, our triple-branch siamese is trained to learn speaker embeddings using triplet loss. We provide results of our systems on VoxCeleb-1 test, VoxSRC-20 validation and test sets.This work was supported by the project PID2019-107579RBI00 / AEI / 10.13039/501100011033Preprin

Prostate cancer detection using deep learning

Cancer detection is one of the principal topics of research in medical science. May it be breast, lung, brain or prostate cancer, advances are being made to improve detection precision and time. Research is being carried out on broad range of procedures at different stages of cancer to understand it better. Prostate cancer, in particular, has seen some novel approaches of detection using both magnetic resonance imaging (MRI) and histopathology data. The approaches include detection using deep neural networks, deep convolutional neural networks in particular because of their human level precision in image recognition task. In this thesis, we analysed a dataset containing multiparametric magnetic resonance imaging (mpMRI) prostate scans. The objective of the research was Gleason grade group classification, through mpMRI scans, which has not been attempted before on a small dataset. We first trained several conventional machine learning algorithms on handcrafted features from the dataset to predict the Gleason grade group of the cases. After that the dataset was augmented using two different augmentation techniques for further experimentation with deep convolutional neural networks. Convolutional neural network of varying depth were used to understand the effects of network depth on classification accuracy. Furthermore, we made an attempt to shed light on the pitfalls of using small dataset for solving problems of such nature

Self-supervised deep learning approaches to speaker recognition: A Ph.D. Thesis overview

Recent advances in Deep Learning (DL) for speaker recognition have improved the performance but are constrained to the need of labels for the background data, which is difficult in prac- tice. In i-vector based speaker recognition, cosine (unsuper- vised) and PLDA (supervised) are the basic scoring techniques, with a big performance gap between the two. In this thesis we tried to fill this gap without using speaker labels in several ways. We applied Restricted Boltzmann Machine (RBM) vectors for the tasks of speaker clustering and tracking in TV broadcast shows. The experiments on AGORA database show that us- ing this approach we gain a relative improvement of 12% and 11% for speaker clustering and tracking tasks, respectively. We also applied DL techniques in order to increase the discrimina- tive power of i-vectors in speaker verification task, for which we have proposed the use of autoencoder in several ways, i.e., (1) as a pre-training for a Deep Neural Network (DNN), (2) as a near- est neighbor autoencoder for i-vectors, (3) as an average pooled nearest neighbor autoencoder. The experiments on VoxCeleb database show that we gain a relative improvement of 21%, 42% and 53%, using the three systems respectively. Finally we also proposed a self-supervised end-to-end speaker verification system. The architecture is based on a Convolutional Neural Network (CNN), trained as a siamese network with multiple branches. From the results we can see that our system shows comparable performance to a supervised baselineThis work was supported by the project PID2019-107579RBI00 / AEI / 10.13039/501100011033Peer ReviewedPostprint (published version

Prostate cancer detection using deep learning

Cancer detection is one of the principal topics of research in medical science. May it be breast, lung, brain or prostate cancer, advances are being made to improve detection precision and time. Research is being carried out on broad range of procedures at different stages of cancer to understand it better. Prostate cancer, in particular, has seen some novel approaches of detection using both magnetic resonance imaging (MRI) and histopathology data. The approaches include detection using deep neural networks, deep convolutional neural networks in particular because of their human level precision in image recognition task. In this thesis, we analysed a dataset containing multiparametric magnetic resonance imaging (mpMRI) prostate scans. The objective of the research was Gleason grade group classification, through mpMRI scans, which has not been attempted before on a small dataset. We first trained several conventional machine learning algorithms on handcrafted features from the dataset to predict the Gleason grade group of the cases. After that the dataset was augmented using two different augmentation techniques for further experimentation with deep convolutional neural networks. Convolutional neural network of varying depth were used to understand the effects of network depth on classification accuracy. Furthermore, we made an attempt to shed light on the pitfalls of using small dataset for solving problems of such nature