
Sixth International Conference on Spoken Language Processing (ICSLP 2000)
Beijing, China
October 1620, 2000 

MultiGroup Mixture Weight HMM
Ming Li, Tiecheng Yu
Speech Processing Lab, Institute of Acoustics, Chinese Academy of Sciences
Beijing, China
This paper presents a new modeling method of the continuous
density Hidden Markov Model. As we know, speech signal is
characterized by a hidden state sequence and each state is
described by the mixture of weighted Gaussian density
functions. Usually if we want to describe speech signal more
precisely, we need to use more Gaussian functions for each state.
But it will increase the computation significantly. On the other
hand, the weight of each Gaussian component is the statistical
average of Gaussian component probabilities for the whole
training data. So it just can depict the average characteristics of
speech signal. For some speech signal these weights are not
proper in fact. Therefore, we propose Multigroup Mixture
Weight HMM to solve this problem. In this kind of HMM, each
state has several groups of mixture weight for the Gaussian
components and it only needs very little additional computation.
In our experiments, it achieved 12% reduction for errors.
Full Paper
Bibliographic reference.
Li, Ming / Yu, Tiecheng (2000):
"Multigroup mixture weight HMM",
In ICSLP2000, vol.1, 290292.