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  • 1
    UID:
    b3kat_BV015771913
    Format: XI, 144 S. , graph. Darst.
    ISBN: 3540003258
    Series Statement: Lecture notes in computer science 2560
    Note: Includes bibliographical references and index
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Mensch-Maschine-Kommunikation ; Natürlichsprachiges System ; Sprecheradaption ; Non-native speaker ; Robustheit ; Bereichsschätzung ; Hochschulschrift
    URL: Cover
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    UID:
    gbv_1649189311
    Format: Online-Ressource
    ISBN: 9783540362906 , 3540003258
    Series Statement: Lecture Notes in Computer Science 2560
    Content: Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system
    Note: Literaturverz. S. [113] - 123
    Additional Edition: ISBN 9783540003250
    Additional Edition: Buchausg. u.d.T. Goronzy, Silke Robust adaptation to non-native accents in automatic speech recognition Berlin : Springer, 2002 ISBN 3540003258
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Mensch-Maschine-Kommunikation ; Natürlichsprachiges System ; Sprecheradaption ; Non-native speaker ; Robustheit ; Bereichsschätzung ; Automatische Spracherkennung ; Natürliche Sprache ; Sprachverarbeitung ; Hochschulschrift
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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  • 3
    UID:
    gbv_358379377
    Format: XI, 144 S , graph. Darst
    ISBN: 3540003258
    Series Statement: Lecture notes in computer science 2560
    Note: Zugl.: Braunschweig, Techn. Univ., Diss., 2002
    Additional Edition: Erscheint auch als Online-Ausgabe Goronzy, Silke Robust Adaptation to Non-Native Accents in Automatic Speech Recognition Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2002 ISBN 9783540362906
    Additional Edition: ISBN 3540003258
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Mensch-Maschine-Kommunikation ; Natürlichsprachiges System ; Sprecheradaption ; Non-native speaker ; Robustheit ; Bereichsschätzung ; Automatische Spracherkennung ; Natürliche Sprache ; Sprachverarbeitung ; Hochschulschrift
    URL: Cover
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  • 4
    Book
    Book
    Berlin [u.a.] : Springer-Verlag
    UID:
    kobvindex_ZLB13466217
    Format: XI, 144 Seiten , Ill., graph. Darst. , 24 cm
    ISBN: 3540003258
    Series Statement: Lecture notes in computer science
    Note: Literaturverz. S. 113 - 123 , Text engl.
    Language: German
    Keywords: Mensch-Maschine-Kommunikation ; Natürlichsprachiges System ; Sprecheradaption ; Non-native speaker ; Robustheit ; Bereichsschätzung
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  • 5
    Online Resource
    Online Resource
    Berlin, Heidelberg :Springer Berlin Heidelberg,
    UID:
    almahu_9947920451202882
    Format: XI, 146 p. , online resource.
    ISBN: 9783540362906
    Series Statement: Lecture Notes in Computer Science, 2560
    Content: Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
    Note: ASR:AnOverview -- Pre-processing of the Speech Data -- Stochastic Modelling of Speech -- Knowledge Bases of an ASR System -- Speaker Adaptation -- Confidence Measures -- Pronunciation Adaptation -- Future Work -- Summary -- Databases and Experimental Settings -- MLLR Results -- Phoneme Inventory.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783540003250
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    Online Resource
    Online Resource
    Berlin, Heidelberg :Springer Berlin Heidelberg :
    UID:
    almahu_9948621575002882
    Format: XI, 146 p. , online resource.
    Edition: 1st ed. 2002.
    ISBN: 9783540362906
    Series Statement: Lecture Notes in Artificial Intelligence ; 2560
    Content: Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems. In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
    Note: ASR:AnOverview -- Pre-processing of the Speech Data -- Stochastic Modelling of Speech -- Knowledge Bases of an ASR System -- Speaker Adaptation -- Confidence Measures -- Pronunciation Adaptation -- Future Work -- Summary -- Databases and Experimental Settings -- MLLR Results -- Phoneme Inventory.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9783662205549
    Additional Edition: Printed edition: ISBN 9783540003250
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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