Výsledky vyhledávání - "Martin Schafföner"

Kernel least-squares models using updates of the pseudoinverse

Autor: Edin Andelic, Marcel Katz, Andreas Wendemuth, Sven E. Krüger, Martin Schafföner

Publikováno v: Neural computation. 18(12)

Sparse nonlinear classification and regression models in reproducing kernel Hilbert spaces (RKHSs) are considered. The use of Mercer kernels and the square loss function gives rise to an overdetermined linear least-squares problem in the correspondin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74da3b6a5295740a9336cf3c4b526418
https://pubmed.ncbi.nlm.nih.gov/17052152

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Limited Training Data Robust Speech Recognition Using Kernel-Based Acoustic Models

Autor: Andreas Wendemuth, Martin Schafföner, Edin Andelic, Sven E. Krüger, Marcel Katz

Publikováno v: ICASSP (1)

Contemporary automatic speech recognition uses hidden-Markov-models (HMMs) to model the temporal structure of speech where one HMM is used for each phonetic unit. The states of the HMMs are associated with state-conditional probability density functi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ebb81c893f77b874390c526bf1bf3c08
https://doi.org/10.1109/icassp.2006.1660226

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Sparse Kernel Logistic Regression using Incremental Feature Selection for Text-Independent Speaker Identification

Autor: Andreas Wendemuth, Martin Schafföner, Sven E. Krüger, Marcel Katz, Edin Andelic

Publikováno v: Odyssey

Logistic regression is a well known classification method in the field of statistical learning. Recently, a kernelized version of logistic regression has become very popular, because it allows non-linear probabilistic classification and shows promisi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2e1206db581ac2f38d24c6dfd619dbd2
https://doi.org/10.1109/odyssey.2006.248115

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Mixture of Support Vector Machines for HMM based Speech Recognition

Autor: Martin Schafföner, Marcel Katz, Edin Andelic, Andreas Wendemuth, Sven E. Krüger

Publikováno v: ICPR (4)

Speech recognition is usually based on Hidden Markov Models (HMMs), which represent the temporal dynamics of speech very efficiently, and Gaussian mixture models, which do non-optimally the classification of speech into single speech units (phonemes)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3cdd14648c02b50fdf4e41154ed7302d
https://doi.org/10.1109/icpr.2006.804

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