Investigations on discriminative training criteria

In this work, a framework for efficient discriminative training and modeling is developed and implemented for both small and large vocabulary continuous speech recognition. Special attention will be directed to the comparison and formalization of varying discriminative training criteria and corresponding optimization methods, discriminative acoustic model evaluation and feature extraction. A formally unifying approach for a class of discriminative training criteria including Maximum Mutual Information (MMI) and Minimum Classification Error (MCE) criterion is presented, including the optimization methods gradient descent (GD) and extended Baum-Welch (EB) algorithm. Using discriminative criteria, novel approaches to splitting of mixture Gaussian densities and to linear feature transformation are derived. Furthermore, efficient algorithms for the application of discriminative training to speech recognition with both small and large vocabulary are developed. Finally, a novel evaluation method for the stochastic models used in speech recognition is derived using methods related to discriminative training. Experiments have been carried out on the TI digit string corpus for American English continuous digit strings, the SieTill corpus for telephone line recorded German continuous digit strings, the Verbmobil corpus for German spontaneous speech and the Wall Street Journal corpus for American English read speech.

Grantor dc:publisher

Publikationsserver der RWTH Aachen University

Year dc:date

2000

Author dc:creator

• Schlüter, Ralf

Contributors dc:contributor

• Ney, Hermann

• info:eu-repo/classification/ddc/530
• Physik
• Automatische Spracherkennung
• Maschinelles Lernen
• Merkmalsextraktion
• Diskriminanzanalyse

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

eng

Identifier

https://publications.rwth-aachen.de/search?p=id:%22RWTH-CONV-114353%22

OAI record oai:identifier

https://publications.rwth-aachen.de/oai2d?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Apublications.rwth-aachen.de%3A52111