### ASA 126th Meeting Denver 1993 October 4-8

## 2pSP3. Speech recognition using hidden Markov models with multiple-track
polynomial regression functions as nonstationary states.

**M. Aksmanovic
L. Deng
**

**
**
*Dept. of Elec. and Comput. Eng., Univ. of Waterloo, Waterloo, ON N2L 3G1,
Canada
*

*
*
The formulation of the hidden Markov model (HMM) has been successfully
used in automatic speech recognition for almost two decades. In the standard
formulation, the individual states in the HMM are each associated with a
generally distinct but stationary stochastic process. This makes the standard
HMM inadequate for representing the nonstationary property of the many speech
segments intended to be described by the HMM-state statistics. A generalized
HMM has been developed to overcome this inadequacy by introducing
state-dependent polynomial regression functions on time that serve as the
time-varying means in the HMM's Gaussian output distributions [e.g., L. Deng,
Signal Process. 27, 65--78 (1992)]. Recently, Aksmanovic and Deng extended the
above model so that the state-dependent nonstationary process contains multiple
tracks of the polynomial functions. This new parametric class of
nonstationary-state HMMs has been implemented and evaluated. Experiments on
fitting models to speech data, on limited-vocabulary word recognition, and on
phonetic classification demonstrated advantages of the nonstationary-state HMMs
over the traditional stationary-state HMMs. Details of the model implementation
and of the experimental results will be described. In particular, the focus
will be on comparisons between uses of single-track and multiple-track
regression functions defined within the HMM states, and on comparisons among
uses of varying orders of the state-dependent polynomial regression functions.