For hidden Markov model (HMM) based speech recognition where the basic speech unit is smaller than the recognizer's output unit, the standard full Baum-Welch re-estimation procedure for the HMM training is very costly in computation. This is hecause it requires evaluation of the HMM output densities
An algorithm for estimating parameters of state-space models
β Scribed by Lilian Shiao-Yen Wu; Jeffrey S. Pai; J.R.M. Hosking
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 449 KB
- Volume
- 28
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Stochastic volatility models (SVMs) represent an important framework for the analysis of financial time series data, together with ARCH-type models; but unlike the latter, the former, at least from the statistical point of view, cannot rely on the possibility of obtaining exact inference, in particu
A fast and efficient adapti¨e sampling algorithm is presented. This algorithm is applied to the aggressi¨e space mapping technique to minimize the number and to automate the selection of frequency sample points of the fine model, thus impro¨ing the efficiency of space mapping. The new technique is a
The paper reviews and generalizes recent filtering and smoothing algorithms for observations generated by a state model. In particular the paper discusses the modified Kalman filter derived by Ansley and Kohn (1985) and Kohn and Ansley (1986) to deal with state space models having partially diffuse