Reduced semi-continuous models for large vocabulary continuous speech recognition in Dutch

被引：0
|
作者：
Demuynck, K
Duchateau, J
VanCompernolle, D
机构：
来源：
ICSLP 96 - FOURTH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, PROCEEDINGS, VOLS 1-4 | 1996年
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D O I：
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中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
Semi-continuous Density HMM's have - due to the decoupling between the set of gaussians and the other HMM-parameters - more possibilities than Continuous Density HMM's to match the number of parameters in the model to the available train data The computational load of the SC-HMM's however is huge compared to the load of their continuous counterparts, because of the large mixture weighting vector and because of the fact that for each frame all gaussians have to be evaluated. This paper describes the different steps taken to reduce the computational load of the SC-HMM's, resulting in faster and better models.
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页码：2289 / 2292
页数：4
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