Beschreibung

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.

Includes supplementary material: sn.pub/extras

Inhalt
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.

Produktinformationen

Titel:	Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
Autor:	Silke Goronzy
EAN:	9783540003250
ISBN:	3540003258
Format:	Kartonierter Einband
Herausgeber:	Springer Berlin Heidelberg
Genre:	Informatik
Anzahl Seiten:	164
Gewicht:	260g
Größe:	H235mm x B155mm x T9mm
Jahr:	2002
Untertitel:	Englisch
Auflage:	2002