ARMA lattice modeling for isolated word speech recognition

Author

Kwan, H.K. ; Li, Tracy X.

Author_Institution

Dept. of Electr. & Comput. Eng., Windsor Univ., Ont., Canada

Volume

3

fYear

2000

fDate

2000

Firstpage

1186

Abstract

In this paper, we introduce an auto-regressive moving average (ARMA) lattice model for speech modeling. The speech characteristics are modeled and expressed in the form of lattice reflection coefficients for classification. Self Organization Map (SOM) is used to build codebooks for classification and recognition of the lattice reflection coefficients. Experimental results based on an isolated word speech database of 10 words/names indicate that the ARMA lattice model achieves superior recognition performance as compared to those of the conventional auto-regressive (AR) model

Keywords

autoregressive moving average processes; modelling; speech recognition; ARMA lattice model; auto-regressive moving average lattice model; classification; codebooks; isolated word speech recognition; lattice reflection coefficients; recognition performance; self-organization map; speech characteristics; speech modeling; Central Processing Unit; Databases; Filters; Lattices; Linear predictive coding; Poles and zeros; Reflection; Resonance; Speech analysis; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2000. Proceedings of the 43rd IEEE Midwest Symposium on

Conference_Location

Lansing, MI

Print_ISBN

0-7803-6475-9

Type

conf

DOI

10.1109/MWSCAS.2000.951427

Filename

951427