Title
Improvements in connected digit recognition using higher order spectral and energy features
Abstract
It is shown how one can apply the improved acoustic modeling techniques (using a continuous density hidden Markov model framework) developed for large vocabulary speech recognition applications to the problem of connected digit recognition with no changes made to the basic modeling techniques and with no vocabulary specific information used. The improved modeling techniques adopted in this study include an improved feature analysis procedure, which incorporates higher order cepstral and log energy time derivatives, and an improved acoustic resolution procedure, which uses more Gaussian mixture components per state to characterize the acoustic variability in each state of the model. Using these techniques, string accuracies of 98.6% for unknown length strings and 99.2% for known length strings were achieved on the standard Texas Instruments connected digits database.
Year
DOI
Venue
1991
10.1109/ICASSP.1991.150348
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Keywords
DocType
ISSN
Markov processes,speech recognition,Gaussian mixture components,Texas Instruments connected digits database,acoustic variability,connected digit recognition,continuous density hidden Markov model framework,higher order spectral time derivatives,improved acoustic modeling,improved feature analysis,known length strings,large vocabulary speech recognition,log energy time derivatives,string accuracies,unknown length strings
Conference
1520-6149
ISBN
Citations 
PageRank 
0-7803-0003-3
31
15.75
References 
Authors
3
3
Name
Order
Citations
PageRank
Jay G. Wilpon1450151.66
Chin-Hui Lee26101852.71
L. R. Rabiner33115.75