Filip Korkmazsky

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This paper presents a novel approach to modeling speech data by Dynamic Bayesian Networks. Instead of defining a specific set of factors that affect speech signals the factors are modeled implicitly by speech data clustering. Different data clusters correspond to different subsets of the factor values. These subsets are represented by the corresponding(More)
The Neologos project is a speech database creation project for the French language, resulting from a collaboration between universities and industrial companies and supported by the French Ministry of Research. The goal of Neologos is to rethink the design of the speech databases in order to enable the development of new algorithms in the field of speech(More)
This paper proposes two new approaches to rapid speaker adaptation of acoustic models by using genetic algorithms. Whereas conventional speaker adaptation techniques yield adapted models which represent local optimum solutions, genetic algorithms are capable to provide multiple optimal solutions , thereby delivering potentially more robust adapted models.(More)
This paper presents a novel approach to speech data normalization by introducing interpolation for histogram equalization. We study different ways of histogram interpolation that inhence this normalization technique. We found that using a special weighting factor to combine current and past test sentence statistics improved speech recognition performance.(More)
In this paper we present improvements of a frame-synchronous noise compensation algorithm that uses Stochastic Matching approach to cope with time-varying unknown noise. We propose to estimate a hierarchical mapping function in parallel with Viterbi alignment. The structure of the transformation tree is build from the states of acoustical models. The(More)
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