Name
Abstract | ||
|---|---|---|
In this work, a new method for estimating the time-varying AR model of speech is presented. Here, the time-varying parameters are modeled as stationary processes. Both the time-varying parameters and their corresponding stationary process are modeled through a common Gauss-Markov model whose state-vector can be estimated through the extended Kalman Filter (EKF) algorithm. The proposed algorithm is different from the earlier methods which use the EKF algorithm. Simulation studies are carried out for both voiced and unvoiced speech. It is shown that the proposed method has less mean-square prediction error than that obtained through the LPC method. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICASSP.2003.1201679 | ICASSP '03). 2003 IEEE International Conference |
Keywords | Field | DocType |
Gaussian distribution,Kalman filters,Markov processes,autoregressive processes,mean square error methods,speech processing,state estimation,time-varying filters,EKF algorithm,Gauss-Markov modeling,extended Kalman Filter,mean-square prediction error,speech,state-vector estimation,stationary process,stationary processes,time-varying AR models,time-varying parameters,unvoiced speech,voiced speech | Speech processing,Autoregressive model,Extended Kalman filter,Speech coding,Pattern recognition,Computer science,Markov model,Stationary process,Kalman filter,Artificial intelligence,Linear predictive coding | Conference |
Volume | ISSN | ISBN |
6 | 1520-6149 | 0-7803-7663-3 |
Citations | PageRank | References |
5 | 0.84 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krishna M. Malladi | 1 | 5 | 0.84 |
| R. V. Rajakumar | 2 | 7 | 1.22 |