9783838101910 - Discriminative Classifiers for Speaker Recognition de Katz, Marcel (6 resultados)

Taschenbuch. Condición: Neu. Discriminative Classifiers for Speaker Recognition | Marcel Katz | Taschenbuch | 164 S. | Deutsch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838101910 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Paperback. Condición: Brand New. 160 pages. German language. 8.66x5.91x0.37 inches. In Stock.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Due to the growing need for security applications, speaker recognition as the biometric task of authenticating a claimant by voice has currently become a focus of interest. In this book we present new approaches to integrate discriminative classifiers like Support Vector Machines (SVMs) and Sparse Kernel Logistic Regression (SKLR) into speaker recognition systems that are traditionally based on generative classifiers like Gaussian Mixture Models (GMMs). In a first approach for limited training data the discriminative classifiers are applied directly on feature vectors from parameterized speech frames and it is shown that both, SVM as well as SKLR outperform traditional methods. In the second approach a state-of-the-art speaker recognition system for large amount of training data is designed that combines Gaussian Mixture Models with discriminative classifiers. Furthermore, we investigate different feature extraction methods for speaker recognition on large amount of training data and it is shown that the application of fusion schemes that combine these subsystems yield a significant improvement of the recognition performance in comparison to the application of single subsystems. 164 pp. Deutsch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Due to the growing need for security applications, speaker recognition as the biometric task of authenticating a claimant by voice has currently become a focus of interest. In this book we present new approaches to integrate discriminative classifiers like .

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Due to the growing need for security applications, speaker recognition as the biometric task of authenticating a claimant by voice has currently become a focus of interest. In this book we present new approaches to integrate discriminative classifiers like Support Vector Machines (SVMs) and Sparse Kernel Logistic Regression (SKLR) into speaker recognition systems that are traditionally based on generative classifiers like Gaussian Mixture Models (GMMs). In a first approach for limited training data the discriminative classifiers are applied directly on feature vectors from parameterized speech frames and it is shown that both, SVM as well as SKLR outperform traditional methods. In the second approach a state-of-the-art speaker recognition system for large amount of training data is designed that combines Gaussian Mixture Models with discriminative classifiers. Furthermore, we investigate different feature extraction methods for speaker recognition on large amount of training data and it is shown that the application of fusion schemes that combine these subsystems yield a significant improvement of the recognition performance in comparison to the application of single subsystems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 164 pp. Deutsch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Due to the growing need for security applications, speaker recognition as the biometric task of authenticating a claimant by voice has currently become a focus of interest. In this book we present new approaches to integrate discriminative classifiers like Support Vector Machines (SVMs) and Sparse Kernel Logistic Regression (SKLR) into speaker recognition systems that are traditionally based on generative classifiers like Gaussian Mixture Models (GMMs). In a first approach for limited training data the discriminative classifiers are applied directly on feature vectors from parameterized speech frames and it is shown that both, SVM as well as SKLR outperform traditional methods. In the second approach a state-of-the-art speaker recognition system for large amount of training data is designed that combines Gaussian Mixture Models with discriminative classifiers. Furthermore, we investigate different feature extraction methods for speaker recognition on large amount of training data and it is shown that the application of fusion schemes that combine these subsystems yield a significant improvement of the recognition performance in comparison to the application of single subsystems.