UID:
almahu_9948621575002882
Umfang:
XI, 146 p.
,
online resource.
Ausgabe:
1st ed. 2002.
ISBN:
9783540362906
Serie:
Lecture Notes in Artificial Intelligence ; 2560
Inhalt:
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.
Anmerkung:
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
Weitere Ausg.:
Printed edition: ISBN 9783662205549
Weitere Ausg.:
Printed edition: ISBN 9783540003250
Sprache:
Englisch
DOI:
10.1007/3-540-36290-8
URL:
https://doi.org/10.1007/3-540-36290-8
