Title :
A linear predictive HMM for vector-valued observations with applications to speech recognition
Author :
Kenny, Patrick ; Lennig, Matthew ; Mermelstein, Paul
Author_Institution :
INRS-Telecommun., Montreal, Que., Canada
fDate :
2/1/1990 12:00:00 AM
Abstract :
The authors describe a new type of Markov model developed to account for the correlations between successive frames of a speech signal. The idea is to treat the sequence of frames as a nonstationary autoregressive process whose parameters are controlled by a hidden Markov chain. It is shown that this type of model performs better than the standard multivariate Gaussian HMM (hidden Markov model) when it is incorporated into a large-vocabulary isolated-word recognizer
Keywords :
Markov processes; filtering and prediction theory; speech recognition; HMM; large-vocabulary isolated-word recognizer; linear predictive hidden Markov model; nonstationary autoregressive process; speech recognition; vector-valued observations; Autoregressive processes; Councils; Helium; Hidden Markov models; Large-scale systems; Pattern recognition; Sampling methods; Speech recognition; Statistical analysis; Stochastic processes;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on
