DocumentCode
3522443
Title
On modeling duration in context in speech recognition
Author
Picone, Joseph
Author_Institution
Texas Instrum. Inc, Dallas, TX, USA
fYear
1989
fDate
23-26 May 1989
Firstpage
421
Abstract
A clustering algorithm is introduced that allows clustering of HMM (hidden Markov models) models directly. This clustering algorithm determines the appropriate duration profile for a recognition unit. High-performance speaker-independent digit recognition on a studio-quality connected-digit database is demonstrated using this algorithm
Keywords
Markov processes; speech recognition; HMM model; clustering algorithm; contextual effects; duration profile; hidden Markov models; seed models; speaker-independent digit recognition; speech recognition; studio-quality connected-digit database; Clustering algorithms; Context modeling; Degradation; Hidden Markov models; Instruments; Laboratories; Power system modeling; Spatial databases; Speech recognition; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on
Conference_Location
Glasgow
ISSN
1520-6149
Type
conf
DOI
10.1109/ICASSP.1989.266455
Filename
266455
Link To Document