Isbn: 9783659921711 - text dependent speaker recognition using deep neural networks (9 risultati)

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Taschenbuch. Condizione: Neu. Text Dependent Speaker Recognition using Deep Neural Networks | Anilkumar Suthar | Taschenbuch | 52 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783659921711 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Speaker Recognition is used for identification of a person depending on the characteristics contained in the speech signal. In this paper we propose the use of Deep Neural Network (DNN) for text dependent speaker Recognition system (SRS). Mel Frequency Cepstral Coefficients (MFCC) and Auto-encoder (Butterfly Structure Neural Network) are used to extract the features of speech signal at the initial stage. The previously obtained coefficients are then used to train the DNN to later classify the speakers. DNN can be directly used to extract features and classify speakers but the MFCC and Auto-encoder are used initially for data compression and maximum number of feature extraction thus aiming to get better efficiency and faster results. 52 pp. Englisch.…

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Suthar AnilkumarDr. Anilkumar Suthar is a Supervisor and Director of L J Institute of Engineering & Technology.Ms.K.Kansara is Post Graduate student in Department of Electronics & Communication Engineering.Speaker Recognition is .…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Speaker Recognition is used for identification of a person depending on the characteristics contained in the speech signal. In this paper we propose the use of Deep Neural Network (DNN) for text dependent speaker Recognition system (SRS). Mel Frequency Cepstral Coefficients (MFCC) and Auto-encoder (Butterfly Structure Neural Network) are used to extract the features of speech signal at the initial stage. The previously obtained coefficients are then used to train the DNN to later classify the speakers. DNN can be directly used to extract features and classify speakers but the MFCC and Auto-encoder are used initially for data compression and maximum number of feature extraction thus aiming to get better efficiency and faster results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Speaker Recognition is used for identification of a person depending on the characteristics contained in the speech signal. In this paper we propose the use of Deep Neural Network (DNN) for text dependent speaker Recognition system (SRS). Mel Frequency Cepstral Coefficients (MFCC) and Auto-encoder (Butterfly Structure Neural Network) are used to extract the features of speech signal at the initial stage. The previously obtained coefficients are then used to train the DNN to later classify the speakers. DNN can be directly used to extract features and classify speakers but the MFCC and Auto-encoder are used initially for data compression and maximum number of feature extraction thus aiming to get better efficiency and faster results.…