Speaker diarization: Segmentation and clustering of speeches

Speaker diarization is the process of sorting speeches according to the speaker. Diarization helps to search and retrieve what a certain speaker uttered in a meeting. Applications of diarization systemsextend to other domains than meetings, for example, lectures, telephone, television, and radio. Besides, diarization enhances the performance of several speech technologies such as speaker recognition, automatic transcription, and speaker tracking. Methodologies previously used in developing diarization systems are discussed. Prior results and techniques are studied and compared. Methods such as Hidden Markov Models and Gaussian Mixture Models that are used in speaker recognition and other speech technologies are also used in speaker diarization. The objective of this thesis is to develop a speaker diarization system in meeting domain. Experimental part of this work indicates that zero-crossing rate can be used effectively in breaking down the audio stream into segments, and adaptive Gaussian Models fit adequately short audio segments. Results show that 35 Gaussian Models and one second as average length of each segment are optimum values to build a diarization system for the tested data. Uniting the segments which are uttered by same speaker is done in a bottom-up clustering by a newapproach of categorizing the mixture weights.

Clustering and International Competitiveness of Information Technology Industry in the Saint Petersburg Area

The main objective of this study is to assess the potential of the information technology industry in the Saint Petersburg area to become one of the new key industries in the Russian economy. To achieve this objective, the study analyzes especially the international competitiveness of the industry and the conditions for clustering. Russia is currently heavily dependent on its natural resources, which are the main source of its recent economic growth. In order to achieve good long-term economic performance, Russia needs diversification in its well-performing industries in addition to the ones operating in the field of natural resources. The Russian government has acknowledged this and started special initiatives to promote such other industries as information technology and nanotechnology. An interesting industry that is basically less than 20 years old and fast growing in Russia, is information technology. Information technology activities and markets are mainly concentrated in Russia’s two biggest cities, Moscow and Saint Petersburg, and areas around them. The information technology industry in the Saint Petersburg area, although smaller than Moscow, is especially dynamic and is gaining increasing foreign company presence. However, the industry is not yet internationally competitive as it lacks substantial and sustainable competitive advantages. The industry is also merely a potential global information technology cluster, as it lacks the competitive edge and a wide supplier and manufacturing base and other related parts of the whole information technology value system. Alone, the industry will not become a key industry in Russia, but it will, on the other hand, have an important supporting role for the development of other industries. The information technology market in the Saint Petersburg area is already large and if more tightly integrated to Moscow, they will together form a huge and still growing market sufficient for most companies operating in Russia currently and in the future. Therefore, the potential of information technology inside Russia is immense.

Clustering and international competitiveness of information technology industry in the Saint Petersburg area

The main objective of this study is to assess the potential of the information technology industry in the Saint Petersburg area to become one of the new key industries in the Russian economy. To achieve this objective, the study analyzes especially the international competitiveness of the industry and the conditions for clustering. Russia is currently heavily dependent on its natural resources, which are the main source of its recent economic growth. In order to achieve good long-term economic performance, Russia needs diversification in its well-performing industries in addition to the ones operating in the field of natural resources. The Russian government has acknowledged this and started special initiatives to promote such other industries as information technology and nanotechnology. An interesting industry that is basically less than 20 years old and fast growing in Russia, is information technology. Information technology activities and markets are mainly concentrated in Russia’s two biggest cities, Moscow and Saint Petersburg, and areas around them. The information technology industry in the Saint Petersburg area, although smaller than Moscow, is especially dynamic and is gaining increasing foreign company presence. However, the industry is not yet internationally competitive as it lacks substantial and sustainable competitive advantages. The industry is also merely a potential global information technology cluster, as it lacks the competitive edge and a wide supplier and manufacturing base and other related parts of the whole information technology value system. Alone, the industry will not become a key industry in Russia, but it will, on the other hand, have an important supporting role for the development of other industries. The information technology market in the Saint Petersburg area is already large and if more tightly integrated to Moscow, they will together form a huge and still growing market sufficient for most companies operating in Russia currently and in the future. Therefore, the potential of information technology inside Russia is immense.

Online peer to peer lending: clustering borrowers using self-organizing maps

The purpose of this thesis is to find out whether all the peer to peer lenders are unworthy of credit and also if there are single qualities or combinations of qualities that determine the probability of default of a person or group of people. Distinguishing qualities are searched with self-organizing maps (SOM). Qualities and groups of people found by the self-organizing map are then compared to the average. The comparison is carried out by looking how big proportion of borrowers meeting the criteria is two months or more behind with their payments. Research data used is collected by an Estonian peer to peer lending company during the years of 2011-2014. Data consists of peer to peer borrowers and information gathered from them.

Fuzzy tolerance/equivalence relations from intersection, union and complement operators and their application to a clustering method

This master thesis work introduces the fuzzy tolerance/equivalence relation and its application in cluster analysis. The work presents about the construction of fuzzy equivalence relations using increasing generators. Here, we investigate and research on the role of increasing generators for the creation of intersection, union and complement operators. The objective is to develop different varieties of fuzzy tolerance/equivalence relations using different varieties of increasing generators. At last, we perform a comparative study with these developed varieties of fuzzy tolerance/equivalence relations in their application to a clustering method.