Share Email Print

Proceedings Paper

Using hidden Markov models based on autoregressive principles for isolated word recognition
Author(s): Evgeny I. Bovbel; Polina P. Tkachova; Igor E. Kheidorov
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The purpose of this paper is to consider autoregressive hidden Markov models for the isolated words recognition task. The training and recognition algorithms for autoregressive hidden Markov models were developed and investigated. The speech feature vector was designed based on the perceptual psychoacoustical principles and arithmetic Fourier transform. The speech data base consisted from 200 belarussian words was created and used for experiments. The developed autoregressive hidden Markov model and introduced speech character vector provide a very high recognition performance.

Paper Details

Date Published: 27 July 1999
PDF: 10 pages
Proc. SPIE 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII, (27 July 1999);
Show Author Affiliations
Evgeny I. Bovbel, Belarusian State Univ. (Belarus)
Polina P. Tkachova, Belarusian State Univ. (Belarus)
Igor E. Kheidorov, Belarusian State Univ. (Belarus)

Published in SPIE Proceedings Vol. 3720:
Signal Processing, Sensor Fusion, and Target Recognition VIII
Ivan Kadar, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?