Paper Info
Title
Speaker adaptation with autonomous model complexity control by MDL principle
Abstract
A speaker adaptation method for continuous density HMMs, which performs well for any amount of data for adaptation, is proposed. This method estimates shift parameters for the means of Gaussian mixture components in the HMM. Each shift parameter is shared by more than one Gaussian components. Many sets of shift parameters with various degree of sharing are prepared, and the set with the appropriate complexity for the gives amount of data is selected using minimum description length (MDL) principle. Unlike previous similar works, the proposed method needs no control parameters for selecting models. A series of 5000-word recognition experiments have demonstrated the effectiveness of this new method.
Year
DOI
Venue
1996
10.1109/ICASSP.1996.543221
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference
Keywords
DocType
Volume
Gaussian processes,adaptive signal processing,hidden Markov models,parameter estimation,speaker recognition,5000-word recognition experiments,Gaussian mixture components,MDL principle,autonomous model complexity control,continuous density HMM,minimum description length,shift parameter estimation,speaker adaptation
Conference
2
ISSN
ISBN
Citations 
1520-6149
0-7803-3192-3
27
PageRank 
References 
Authors
5.20
8
2
Name
Order
Citations
PageRank
Koichi Shinoda146365.14
T. Watanabe225251.28